Bibliography of computer-aided Drug Design

Last additions

• TRAPP: A Tool for Analysis of Transient Binding Pockets in Proteins.
  Kokh, Daria B and Richter, Stefan and Henrich, Stefan and Czodrowski, Paul and Rippmann, Friedrich and Wade, Rebecca C
  Journal of chemical information and modeling, 2013, 53(5), 1235-1252
  PMID: 23621586     doi: 10.1021/ci4000294
  
   
  We present TRAPP (TRAnsient Pockets in Proteins), a new automated software platform for tracking, analysis, and visualization of binding pocket variations along a protein motion trajectory or within an ensemble of protein structures that may encompass conformational changes ranging from local side chain fluctuations to global backbone motions. TRAPP performs accurate grid-based calculations of the shape and physicochemical characteristics of a binding pocket for each structure and detects the conserved and transient regions of the pocket in an ensemble of protein conformations. It also provides tools for tracing the opening of a particular subpocket and residues that contribute to the binding site. TRAPP thus enables an assessment of the druggability of a disease-related target protein taking its flexibility into account.
  
  
• Scaffold-Focused Virtual Screening: Prospective Application to the Discovery of TTK Inhibitors.
  Langdon, Sarah R and Westwood, Isaac M and van Montfort, Rob L M and Brown, Nathan and Blagg, Julian
  Journal of chemical information and modeling, 2013, 53(5), 1100-1112
  PMID: 23672464     doi: 10.1021/ci400100c
  
   
  We describe and apply a scaffold-focused virtual screen based upon scaffold trees to the mitotic kinase TTK (MPS1). Using level 1 of the scaffold tree, we perform both 2D and 3D similarity searches between a query scaffold and a level 1 scaffold library derived from a 2 million compound library; 98 compounds from 27 unique top-ranked level 1 scaffolds are selected for biochemical screening. We show that this scaffold-focused virtual screen prospectively identifies eight confirmed active compounds that are structurally differentiated from the query compound. In comparison, 100 compounds were selected for biochemical screening using a virtual screen based upon whole molecule similarity resulting in 12 confirmed active compounds that are structurally similar to the query compound. We elucidated the binding mode for four of the eight confirmed scaffold hops to TTK by determining their protein-ligand crystal structures; each represents a ligand-efficient scaffold for inhibitor design.
  
  
• Cheminformatics aspects of high throughput screening: from robots to models: symposium summary.
  Jane Tseng, Y and Martin, Eric and G Bologa, Cristian and Shelat, Anang A
  Journal of computer-aided molecular design, 2013
  PMID: 23636795     doi: 10.1007/s10822-013-9646-6
  
   
  The "Cheminformatics aspects of high throughput screening (HTS): from robots to models" symposium was part of the computers in chemistry technical program at the American Chemical Society National Meeting in Denver, Colorado during the fall of 2011. This symposium brought together researchers from high throughput screening centers and molecular modelers from academia and industry to discuss the integration of currently available high throughput screening data and assays with computational analysis. The topics discussed at this symposium covered the data-infrastructure at various academic, hospital, and National Institutes of Health-funded high throughput screening centers, the cheminformatics and molecular modeling methods used in real world examples to guide screening and hit-finding, and how academic and non-profit organizations can benefit from current high throughput screening cheminformatics resources. Specifically, this article also covers the remarks and discussions in the open panel discussion of the symposium and summarizes the following talks on "Accurate Kinase virtual screening: biochemical, cellular and selectivity", "Selective, privileged and promiscuous chemical patterns in high-throughput screening" and "Visualizing and exploring relationships among HTS hits using network graphs".
  
  
• Automated Large-Scale File Preparation, Docking, and Scoring: Evaluation of ITScore and STScore Using the 2012 Community Structure-Activity Resource Benchmark.
  Grinter, Sam Z and Yan, Chengfei and Huang, Sheng-You and Jiang, Lin and Zou, Xiaoqin
  Journal of chemical information and modeling, 2013
  PMID: 23656179     doi: 10.1021/ci400045v
  
   
  In this study, we use the recently released 2012 Community Structure-Activity Resource (CSAR) data set to evaluate two knowledge-based scoring functions, ITScore and STScore, and a simple force-field-based potential (VDWScore). The CSAR data set contains 757 compounds, most with known affinities, and 57 crystal structures. With the help of the script files for docking preparation, we use the full CSAR data set to evaluate the performances of the scoring functions on binding affinity prediction and active/inactive compound discrimination. The CSAR subset that includes crystal structures is used as well, to evaluate the performances of the scoring functions on binding mode and affinity predictions. Within this structure subset, we investigate the importance of accurate ligand and protein conformational sampling and find that the binding affinity predictions are less sensitive to non-native ligand and protein conformations than the binding mode predictions. We also find the full CSAR data set to be more challenging in making binding mode predictions than the subset with structures. The script files used for preparing the CSAR data set for docking, including scripts for canonicalization of the ligand atoms, are offered freely to the academic community.
  
  
• CSAR Data Set Release 2012: Ligands, Affinities, Complexes, and Docking Decoys.
  Dunbar, James B and Smith, Richard D and Damm-Ganamet, Kelly L and Ahmed, Aqeel and Esposito, Emilio Xavier and Delproposto, James and Chinnaswamy, Krishnapriya and Kang, You-Na and Kubish, Ginger and Gestwicki, Jason E and Stuckey, Jeanne A and Carlson, Heather A
  Journal of chemical information and modeling, 2013
  PMID: 23617227     doi: 10.1021/ci4000486
  
   
  A major goal in drug design is the improvement of computational methods for docking and scoring. The Community Structure Activity Resource (CSAR) has collected several data sets from industry and added in-house data sets that may be used for this purpose ( www.csardock.org ). CSAR has currently obtained data from Abbott, GlaxoSmithKline, and Vertex and is working on obtaining data from several others. Combined with our in-house projects, we are providing a data set consisting of 6 protein targets, 647 compounds with biological affinities, and 82 crystal structures. Multiple congeneric series are available for several targets with a few representative crystal structures of each of the series. These series generally contain a few inactive compounds, usually not available in the literature, to provide an upper bound to the affinity range. The affinity ranges are typically 3-4 orders of magnitude per series. For our in-house projects, we have had compounds synthesized for biological testing. Affinities were measured by Thermofluor, Octet RED, and isothermal titration calorimetry for the most soluble. This allows the direct comparison of the biological affinities for those compounds, providing a measure of the variance in the experimental affinity. It appears that there can be considerable variance in the absolute value of the affinity, making the prediction of the absolute value ill-defined. However, the relative rankings within the methods are much better, and this fits with the observation that predicting relative ranking is a more tractable problem computationally. For those in-house compounds, we also have measured the following physical properties: logD, logP, thermodynamic solubility, and pKa. This data set also provides a substantial decoy set for each target consisting of diverse conformations covering the entire active site for all of the 58 CSAR-quality crystal structures. The CSAR data sets (CSAR-NRC HiQ and the 2012 release) provide substantial, publically available, curated data sets for use in parametrizing and validating docking and scoring methods.
  
  
• Investigation on the Effect of Key Water Molecules on Docking Performance in CSARdock Exercise.
  Kumar, Ashutosh and Zhang, Kam Y J
  Journal of chemical information and modeling, 2013
  PMID: 23617355     doi: 10.1021/ci400052w
  
   
  Water molecules are routinely included in molecular docking methods and protocols because of their important role in mediating ligand protein interactions. However, it is still unclear that the inclusion of explicit water molecules improves docking accuracy. To explore the effect of including key water molecules on docking accuracy and performance, we participated in the CSARdock 2011 benchmark exercise. This exercise provides a valuable opportunity for researchers to test their docking programs, methods, and protocols in a blind testing environment. The benchmark exercise and its analysis presented in this paper showed that the performance of current docking programs can be improved by incorporating carefully selected water molecules. Our study showed that water mapping calculations can be used to select key water molecules from experimentally identified water positions for molecular dockings. We have observed that inclusion of all binding site water molecules led to reduced performance and erroneous results. Moreover, an overall improvement in binding pose prediction was achieved when computationally selected water molecules are included during docking simulations. The improvement in the docking performance by including water molecules also depends on protein system, chemical class of ligand, docking method, and scoring function.
  
  
• Chemogenomics Approaches to Rationalizing the Mode-of-Action of Traditional Chinese and Ayurvedic Medicines
  Mohd Fauzi, Fazlin and Koutsoukas, Alexios and Lowe, Robert and Joshi, Kalpana and Fan, Tai-Ping and Glen, Robert C and Bender, Andreas
  Journal of chemical information and modeling, 2013, 53(3), 661-673
  
   
  Traditional Chinese medicine (TCM) and Ayurveda have been used in humans for thousands of years. While the link to a particular indication has been established in man, the mode-of-action (MOA) of the formulations often remains unknown. In this study, we aim to understand the MOA of formulations used in traditional medicine using an in silico target prediction algorithm, which aims to predict protein targets (and hence MOAs), given the chemical structure of a compound. Following this approach we were able to establish several links between suggested MOAs and experimental evidence. In particular, compounds from the 'tonifying and replenishing medicinal' class from TCM exhibit a hypoglycemic effect which can be related to activity of the ingredients against the Sodium-Glucose Transporters (SGLT) 1 and 2 as well as Protein Tyrosine Phosphatase (PTP). Similar results were obtained for Ayurvedic anticancer drugs. Here, both primary anticancer targets (those directly involved in cancer pathogenesis) such as steroid-5-alpha-reductase 1 and 2 were predicted as well as targets which act synergistically with the primary target, such as the efflux pump P-glycoprotein (P-gp). In addition, we were able to elucidate some targets which may point us to novel MOAs as well as explain side effects. Most notably, GPBAR1, which was predicted as a target for both 'tonifying and replenishing medicinal' and anticancer classes, suggests an influence of the compounds on metabolism. Understanding the MOA of these compounds is beneficial as it provides a resource for NMEs with possibly higher efficacy in the clinic than those identified by single-target biochemical assays.
  
  
• Multiple structures for virtual ligand screening: defining binding site properties-based criteria to optimize the selection of the query.
  Ben Nasr, Nesrine and Guillemain, Hélène and Lagarde, Nathalie and Zagury, Jean-François and Montes, Matthieu
  Journal of chemical information and modeling, 2013, 53(2), 293-311
  PMID: 23312043    
  
   
  Virtual ligand screening is an integral part of the modern drug discovery process. Traditional ligand-based, virtual screening approaches are fast but require a set of structurally diverse ligands known to bind to the target. Traditional structure-based approaches require high-resolution target protein structures and are computationally demanding. In contrast, the recently developed threading/structure-based FINDSITE-based approaches have the advantage that they are as fast as traditional ligand-based approaches and yet overcome the limitations of traditional ligand- or structure-based approaches. These new methods can use predicted low-resolution structures and infer the likelihood of a ligand binding to a target by utilizing ligand information excised from the target's remote or close homologous proteins and/or libraries of ligand binding databases. Here, we develop an improved version of FINDSITE, FINDSITEfilt, that filters out false positive ligands in threading identified templates by a better binding site detection procedure that includes information about the binding site amino acid similarity. We then combine FINDSITEfilt with FINDSITEX that uses publicly available binding databases ChEMBL and DrugBank for virtual ligand screening. The combined approach, FINDSITEcomb, is compared to two traditional docking methods, AUTODOCK Vina and DOCK 6, on the DUD benchmark set. It is shown to be significantly better in terms of enrichment factor, dependence on target structure quality, and speed. FINDSITEcomb is then tested for virtual ligand screening on a large set of 3576 generic targets from the DrugBank database as well as a set of 168 Human GPCRs. Excluding close homologues, FINDSITEcomb gives an average enrichment factor of 52.1 for generic targets and 22.3 for GPCRs within the top 1% of the screened compound library. Around 65% of the targets have better than random enrichment factors. The performance is insensitive to target structure quality, as long as it has a TM-score ≥ 0.4 to native. Thus, FINDSITEcomb makes the screening of millions of compounds across entire proteomes feasible. The FINDSITEcomb web service is freely available for academic users at http://cssb.biology.gatech.edu/skolnick/webservice/FINDSITE-COMB/index.html
  
  
• Discovery of Novel Acetohydroxyacid Synthase Inhibitors as Active Agents against Mycobacterium tuberculosis by Virtual Screening and Bioassay
  Wang, Di and Zhu, Xuelian and Cui, Changjun and Dong, Mei and Jiang, Hualiang and Li, Zhengming and Liu, Zhen and Zhu, Weiliang and Wang, Jian-Guo
  Journal of chemical information and modeling, 2013, 53(2), 343-353
  
   
  Acetohydroxyacid synthase (AHAS) has been regarded as a promising drug target against Mycobacterium tuberculosis (MTB) as it catalyzes the biosynthesis of branched-chain amino acids. In this study, 23 novel AHAS inhibitors were identified through molecular docking followed by similarity search. The determined IC50 values range from 0.385 $\pm$ 0.026 ?M to >200 ?M against bacterium AHAS. Five of the identified compounds show significant in vitro activity against H37Rv strains (MICs in the range of 2.5?80 mg/L) and clinical MTB strains, including MDR and XDR isolates. More impressively, compounds 5 and 7 can enhance the killing ability against macrophages infected pathogen remarkably. This study suggests our discovered inhibitors can be further developed as novel anti-MTB therapeutics targeting AHAS.
  
  
• Prediction of compounds with closely related activity profiles using weighted support vector machine linear combinations.
  Heikamp, Kathrin and Bajorath, Jürgen
  Journal of chemical information and modeling, 2013, 53(4), 791-801
  PMID: 23517241     doi: 10.1021/ci400090t
  
   
  Using support vector machine (SVM) ranking, a complex multi-class prediction task has been investigated involving sets of compounds that were active against related targets and represented all possible combinations of single-, dual-, and triple-target activities. Standard SVM models were not capable of differentiating compounds with overlapping yet distinct activity profiles. To address this problem, we designed differentially weighted SVM linear combinations that were found to preferentially detect compounds with desired activity profiles and deprioritize others. Hence, combining independently derived SVM models using negative and positive linear weighting factors balanced relative contributions from individual reference sets and successfully distinguished between compounds with overlapping activity profiles.
  
  
• Use of Experimental Design To Optimize Docking Performance: The Case of LiGenDock, the Docking Module of Ligen, a New De Novo Design Program.
  Beato, Claudia and Beccari, Andrea R and Cavazzoni, Carlo and Lorenzi, Simone and Costantino, Gabriele
  Journal of chemical information and modeling, 2013
  PMID: 23590204     doi: 10.1021/ci400079k
  
   
  On route toward a novel de novo design program, called LiGen, we developed a docking program, LiGenDock, based on pharmacophore models of binding sites, including a non-enumerative docking algorithm. In this paper, we present the functionalities of LiGenDock and its accompanying module LiGenPocket, aimed at the binding site analysis and structure-based pharmacophore definition. We also report the optimization procedure we have carried out to improve the cognate docking and virtual screening performance of LiGenDock. In particular, we applied the design of experiments (DoE) methodology to screen the set of user-adjustable parameters to identify those having the largest influence on the accuracy of the results (which ensure the best performance in pose prediction and in virtual screening approaches) and then to choose their optimal values. The results are also compared with those obtained by two popular docking programs, namely, Glide and AutoDock for pose prediction, and Glide and DOCK6 for Virtual Screening.
  
  
• Systematic identification of proteins that elicit drug side effects.
  Kuhn, Michael and Al Banchaabouchi, Mumna and Campillos, Monica and Jensen, Lars Juhl and Gross, Cornelius and Gavin, Anne-Claude and Bork, Peer
  Molecular systems biology, 2013, 9, 663
  PMID: 23632385     doi: 10.1038/msb.2013.10
  
   
  Side effect similarities of drugs have recently been employed to predict new drug targets, and networks of side effects and targets have been used to better understand the mechanism of action of drugs. Here, we report a large-scale analysis to systematically predict and characterize proteins that cause drug side effects. We integrated phenotypic data obtained during clinical trials with known drug-target relations to identify overrepresented protein-side effect combinations. Using independent data, we confirm that most of these overrepresentations point to proteins which, when perturbed, cause side effects. Of 1428 side effects studied, 732 were predicted to be predominantly caused by individual proteins, at least 137 of them backed by existing pharmacological or phenotypic data. We prove this concept in vivo by confirming our prediction that activation of the serotonin 7 receptor (HTR7) is responsible for hyperesthesia in mice, which, in turn, can be prevented by a drug that selectively inhibits HTR7. Taken together, we show that a large fraction of complex drug side effects are mediated by individual proteins and create a reference for such relations.
  
  
• In silico categorization of in vivo intrinsic clearance using machine learning.
  Hsiao, Ya-Wen and Fagerholm, Urban and Norinder, Ulf
  Molecular Pharmaceutics, 2013, 10(4), 1318-1321
  PMID: 23427914     doi: 10.1021/mp300484r
  
   
  Machine learning has recently become popular and much used within the life science research domain, e.g., for finding quantitative structure-activity relationships (QSARs) between molecular structures and different biological end points. In the work presented here, we have applied orthogonal partial least-squares (OPLS), principal component analysis (PCA), and random forests (RF) methods for classification as well as regression analysis to a publicly available in vivo data set in order to assess the intrinsic metabolic clearance (CLint) in humans. The derived classification models are able to identify compounds with CLint lower and higher than 1500 mL/min, respectively, with nearly 80% accuracy. The most relevant descriptors are of lipophilicity and charge/polarizability types. Furthermore, the accuracy from a classification model based on regression analysis, using the 1500 mL/min cutoff, is also around 80%. These results suggest the usefulness of machine learning techniques to derive robust and predictive models in the area of in vivo ADMET (absorption, distribution, metabolism, elimination, and toxicity) modeling.
  
  
• Development of the Knowledge-Based and Empirical Combined Scoring Algorithm (KECSA) To Score Protein-Ligand Interactions.
  Zheng, Zheng and Merz, Kenneth M
  Journal of chemical information and modeling, 2013
  PMID: 23560465     doi: 10.1021/ci300619x
  
   
  We describe a novel knowledge-based protein-ligand scoring function that employs a new definition for the reference state, allowing us to relate a statistical potential to a Lennard-Jones (LJ) potential. In this way, the LJ potential parameters were generated from protein-ligand complex structural data contained in the Protein Databank (PDB). Forty-nine (49) types of atomic pairwise interactions were derived using this method, which we call the knowledge-based and empirical combined scoring algorithm (KECSA). Two validation benchmarks were introduced to test the performance of KECSA. The first validation benchmark included two test sets that address the training set and enthalpy/entropy of KECSA. The second validation benchmark suite included two large-scale and five small-scale test sets, to compare the reproducibility of KECSA, with respect to two empirical score functions previously developed in our laboratory (LISA and LISA+), as well as to other well-known scoring methods. Validation results illustrate that KECSA shows improved performance in all test sets when compared with other scoring methods, especially in its ability to minimize the root mean square error (RMSE). LISA and LISA+ displayed similar performance using the correlation coefficient and Kendall\tau} as the metric of quality for some of the small test sets. Further pathways for improvement are discussed for which would allow KECSA to be more sensitive to subtle changes in ligand structure.
  
  
• Bioturbo Similarity Searching: Combining Chemical and Biological Similarity To Discover Structurally Diverse Bioactive Molecules.
  Wassermann, Anne Mai and Lounkine, Eugen and Glick, Meir
  Journal of chemical information and modeling, 2013
  PMID: 23461561     doi: 10.1021/ci300607r
  
   
  Virtual screening using bioactivity profiles has become an integral part of currently applied hit finding methods in pharmaceutical industry. However, a significant drawback of this approach is that it is only applicable to compounds that have been biologically tested in the past and have sufficient activity annotations for meaningful profile comparisons. Although bioactivity data generated in pharmaceutical institutions are growing on an unprecedented scale, the number of biologically annotated compounds still covers only a minuscule fraction of chemical space. For a newly synthesized compound or an isolated natural product to be biologically characterized across multiple assays, it may take a considerable amount of time. Consequently, this chemical matter will not be included in virtual screening campaigns based on bioactivity profiles. To overcome this problem, we herein introduce bioturbo similarity searching that uses chemical similarity to map molecules without biological annotations into bioactivity space and then searches for biologically similar compounds in this reference system. In benchmark calculations on primary screening data, we demonstrate that our approach generally achieves higher hit rates and identifies structurally more diverse compounds than approaches using chemical information only. Furthermore, our method is able to discover hits with novel modes of inhibition that traditional 2D and 3D similarity approaches are unlikely to discover. Test calculations on a set of natural products reveal the practical utility of the approach for identifying novel and synthetically more accessible chemical matter.
  
  
• Encoding Protein-Ligand Interaction Patterns in Fingerprints and Graphs.
  Desaphy, Jérémy and Raimbaud, Eric and Ducrot, Pierre and Rognan, Didier
  Journal of chemical information and modeling, 2013
  PMID: 23432543     doi: 10.1021/ci300566n
  
   
  We herewith present a novel and universal method to convert protein-ligand coordinates into a simple fingerprint of 210 integers registering the corresponding molecular interaction pattern. Each interaction (hydrophobic, aromatic, hydrogen bond, ionic bond, metal complexation) is detected on the fly and physically described by a pseudoatom centered either on the interacting ligand atom, the interacting protein atom, or the geometric center of both interacting atoms. Counting all possible triplets of interaction pseudoatoms within six distance ranges, and pruning the full integer vector to keep the most frequent triplets enables the definition of a simple (210 integers) and coordinate frame-invariant interaction pattern descriptor (TIFP) that can be applied to compare any pair of protein-ligand complexes. TIFP fingerprints have been calculated for ca. 10 000 druggable protein-ligand complexes therefore enabling a wide comparison of relationships between interaction pattern similarity and ligand or binding site pairwise similarity. We notably show that interaction pattern similarity strongly depends on binding site similarity. In addition to the TIFP fingerprint which registers intermolecular interactions between a ligand and its target protein, we developed two tools (Ishape, Grim) to align protein-ligand complexes from their interaction patterns. Ishape is based on the overlap of interaction pseudoatoms using a smooth Gaussian function, whereas Grim utilizes a standard clique detection algorithm to match interaction pattern graphs. Both tools are complementary and enable protein-ligand complex alignments capitalizing on both global and local pattern similarities. The new fingerprint and companion alignment tools have been successfully used in three scenarios: (i) interaction-biased alignment of protein-ligand complexes, (ii) postprocessing docking poses according to known interaction patterns for a particular target, and (iii) virtual screening for bioisosteric scaffolds sharing similar interaction patterns.
  
  
• Ligand Binding Site Identification by Higher Dimension Molecular Dynamics.
  Yatawara, Achani K and Hodoscek, Milan and Mierke, Dale F
  Journal of chemical information and modeling, 2013
  PMID: 23394112     doi: 10.1021/ci300561b
  
   
  We propose a new molecular dynamics (MD) protocol to identify the binding site of a guest within a host. The method utilizes a four spatial (4D) dimension representation of the ligand allowing for rapid and efficient sampling within the receptor. We applied the method to two different model receptors characterized by diverse structural features of the binding site and different ligand binding affinities. The Abl kinase domain is comprised of a deep binding pocket and displays high affinity for the two chosen ligands examined here. The PDZ1 domain of PSD-95 has a shallow binding pocket that accommodates a peptide ligand involving far fewer interactions and a micromolar affinity. To ensure completely unbiased searching, the ligands were placed in the direct center of the protein receptors, away from the binding site, at the start of the 4D MD protocol. In both cases, the ligands were successfully docked into the binding site as identified in the published structures. The 4D MD protocol is able to overcome local energy barriers in locating the lowest energy binding pocket and will aid in the discovery of guest binding pockets in the absence of a priori knowledge of the site of interaction.
  
  
• Combination of similarity rankings using data fusion.
  Willett, Peter
  Journal of chemical information and modeling, 2013, 53(1), 1-10
  PMID: 23297768     doi: 10.1021/ci300547g
  
   
  
  
• Application of in silico, in vitro and preclinical pharmacokinetic data for the effective and efficient prediction of human pharmacokinetics.
  Grime, Kenneth H and Barton, Patrick and McGinnity, Dermot F
  Molecular Pharmaceutics, 2013, 10(4), 1191-1206
  PMID: 23253040     doi: 10.1021/mp300476z
  
   
  In the present age of pharmaceutical research and development, focused delivery of decision making data is more imperative than ever before. Resulting from several years' success, failure and consequential learning, this article also proffers advice and guidance on which in vitro and in vivo experiments to perform to facilitate efficient and cost-effective pursuit of candidate drugs with acceptable human pharmacokinetic profiles. Predictive in silico models are important in directing design toward compounds with the highest probability of having suitable DMPK properties rather than in predicting human pharmacokinetics per se, and the value and utility of such approaches are reviewed with the intention of providing direction to DMPK scientists. Relating to absorption, distribution, elimination and effective half-life, strategies are described to provide direction in commonly encountered scenarios.
  
  
• In silico physicochemical parameter predictions.
  Wenlock, Mark C and Barton, Patrick
  Molecular Pharmaceutics, 2013, 10(4), 1224-1235
  PMID: 23305561     doi: 10.1021/mp300537k
  
   
  Drug discovery is a complex process with the aim of discovering efficacious molecules where their potency and selectivity are balanced against ADMET properties to set the appropriate dose and dosing interval. The link between physicochemical properties and molecular structure are well established. The subsequent connections between physicochemical properties and a drug's biological behavior provide an indirect link back to structure, facilitating the prediction of a biological property as a consequence of a particular molecular manipulation. Due to this understanding, during early drug discovery in vitro physicochemical property assays are commonly performed to eliminate compounds with properties commensurate with high attrition risks. However, the goal is to accurately predict physicochemical properties to prevent the synthesis of high risk compounds and hence minimize wasted drug discovery efforts. This paper will review the relevance to ADMET behaviors of key physicochemical properties, such as ionization, aqueous solubility, hydrogen bonding strength and hydrophobicity, and the in silico methodology for predicting them.
  
  
• Integration of in Silico and in Vitro Tools for Scaffold Optimization during Drug Discovery: Predicting P-Glycoprotein Efflux.
  Desai, Prashant V and Sawada, Geri A and Watson, Ian A and Raub, Thomas J
  Molecular Pharmaceutics, 2013, 10(4), 1249-1261
  PMID: 23363443     doi: 10.1021/mp300555n
  
   
  In silico tools are regularly utilized for designing and prioritizing compounds to address challenges related to drug metabolism and pharmacokinetics (DMPK) during the process of drug discovery. P-Glycoprotein (P-gp) is a member of the ATP-binding cassette (ABC) transporters with broad substrate specificity that plays a significant role in absorption and distribution of drugs that are P-gp substrates. As a result, screening for P-gp transport has now become routine in the drug discovery process. Typically, bidirectional permeability assays are employed to assess in vitro P-gp efflux. In this article, we use P-gp as an example to illustrate a well-validated methodology to effectively integrate in silico and in vitro tools to identify and resolve key barriers during the early stages of drug discovery. A detailed account of development and application of in silico tools such as simple guidelines based on physicochemical properties and more complex quantitative structure-activity relationship (QSAR) models is provided. The tools were developed based on structurally diverse data for more than 2000 compounds generated using a robust P-gp substrate assay over the past several years. Analysis of physicochemical properties revealed a significantly lower proportion (<10%) of P-gp substrates among the compounds with topological polar surface area (TPSA) <60\AA}(2) and the most basic cpKa <8. In contrast, this proportion of substrates was greater than 75% for compounds with TPSA >60\AA}(2) and the most basic cpKa >8. Among the various QSAR models evaluated to predict P-gp efflux, the Bagging model provided optimum prediction performance for prospective validation based on chronological test sets. Four sequential versions of the model were built with increasing numbers of compounds to train the models as new data became available. Except for the first version with the smallest training set, the QSAR models exhibited robust prediction profiles with positive prediction values (PPV) and negative prediction values (NPV) exceeding 80%. The QSAR model demonstrated better concordance with the manual P-gp substrate assay than an automated P-gp substrate screen. The in silico and the in vitro tools have been effectively integrated during early stages of drug discovery to resolve P-gp-related challenges exemplified by several case studies. Key learning based on our experience with P-gp can be widely applicable across other DMPK-related challenges.
  
  
• Physicochemical and DMPK In Silico Models: Facilitating Their Use by Medicinal Chemists.
  Ortwine, Daniel F and Aliagas, Ignacio
  Molecular Pharmaceutics, 2013, 10(4), 1153-1161
  PMID: 23402361     doi: 10.1021/mp3006193
  
   
  It is known that the developability of drugs is related to their physicochemical and DMPK properties. Given the time and expense involved in discovering and developing new drugs, maximizing the chance of success by calculating properties ahead of chemical synthesis and testing, and only acting on those candidates whose properties fall into a desired range, would seem to make sense. This paper provides an overview of calculable physicochemical and DMPK properties, an assessment of their relative difficulty of their calculation and accuracy, and available software. Methods companies have employed to communicate results will be discussed, including the use of composite scoring functions and ranking schemes. Calculations do no good if chemists will not use them to prioritize synthesis decisions. Strategies are presented for facilitating model usage. An approach adopted at Genentech for presenting results that involves the close coupling of property calculations with 3D structure based drug design is described.
  
  
• Predictive DMPK: In Silico ADME Predictions in Drug Discovery.
  Kenny, Jane R
  Molecular Pharmaceutics, 2013, 10(4), 1151-1152
  PMID: 23540938     doi: 10.1021/mp400102t
  
   
  
  
• Evolution of ADME Science: Where Else Can Modeling and Simulation Contribute?
  Smith, Dennis A
  Molecular Pharmaceutics, 2013, 10(4), 1162-1170
  PMID: 23294153     doi: 10.1021/mp3005319
  
   
  The commentary describes progress in modeling and simulation in ADME science and focuses on lipoidal permeability as a central driver of drug molecule disposition. The tension between screening and in silico is outlined with practical suggestions on how to improve multiparameter models. The limitations on modeling drug metabolism and its enzymes are highlighted together with key features in molecules that lead to drug transport. Reservations about the quality of data and the imprecise classification of drug molecules are explained. Encouragement to move modeling and simulation to the forefront of project start-up is provided after examining the complexity of macromolecule-small molecule conjugate prodrugs.
  
  
• Prediction of polypharmacological profiles of drugs by the integration of chemical, side effect, and therapeutic space.
  Cheng, Feixiong and Li, Weihua and Wu, Zengrui and Wang, Xichuan and Zhang, Chen and Li, Jie and Liu, Guixia and Tang, Yun
  Journal of chemical information and modeling, 2013, 53(4), 753-762
  PMID: 23527559     doi: 10.1021/ci400010x
  
   
  Prediction of polypharmacological profiles of drugs enables us to investigate drug side effects and further find their new indications, i.e. drug repositioning, which could reduce the costs while increase the productivity of drug discovery. Here we describe a new computational framework to predict polypharmacological profiles of drugs by the integration of chemical, side effect, and therapeutic space. On the basis of our previous developed drug side effects database, named MetaADEDB, a drug side effect similarity inference (DSESI) method was developed for drug-target interaction (DTI) prediction on a known DTI network connecting 621 approved drugs and 893 target proteins. The area under the receiver operating characteristic curve was 0.882 $\pm$ 0.011 averaged from 100 simulated tests of 10-fold cross-validation for the DSESI method, which is comparative with drug structural similarity inference and drug therapeutic similarity inference methods. Seven new predicted candidate target proteins for seven approved drugs were confirmed by published experiments, with the successful hit rate more than 15.9%. Moreover, network visualization of drug-target interactions and off-target side effect associations provide new mechanism-of-action of three approved antipsychotic drugs in a case study. The results indicated that the proposed methods could be helpful for prediction of polypharmacological profiles of drugs.
  
  
• Drug target prediction and repositioning using an integrated network-based approach.
  Emig, Dorothea and Ivliev, Alexander and Pustovalova, Olga and Lancashire, Lee and Bureeva, Svetlana and Nikolsky, Yuri and Bessarabova, Marina
  PloS one, 2013, 8(4), e60618
  PMID: 23593264     doi: 10.1371/journal.pone.0060618
  
   
  The discovery of novel drug targets is a significant challenge in drug development. Although the human genome comprises approximately 30,000 genes, proteins encoded by fewer than 400 are used as drug targets in the treatment of diseases. Therefore, novel drug targets are extremely valuable as the source for first in class drugs. On the other hand, many of the currently known drug targets are functionally pleiotropic and involved in multiple pathologies. Several of them are exploited for treating multiple diseases, which highlights the need for methods to reliably reposition drug targets to new indications. Network-based methods have been successfully applied to prioritize novel disease-associated genes. In recent years, several such algorithms have been developed, some focusing on local network properties only, and others taking the complete network topology into account. Common to all approaches is the understanding that novel disease-associated candidates are in close overall proximity to known disease genes. However, the relevance of these methods to the prediction of novel drug targets has not yet been assessed. Here, we present a network-based approach for the prediction of drug targets for a given disease. The method allows both repositioning drug targets known for other diseases to the given disease and the prediction of unexploited drug targets which are not used for treatment of any disease. Our approach takes as input a disease gene expression signature and a high-quality interaction network and outputs a prioritized list of drug targets. We demonstrate the high performance of our method and highlight the usefulness of the predictions in three case studies. We present novel drug targets for scleroderma and different types of cancer with their underlying biological processes. Furthermore, we demonstrate the ability of our method to identify non-suspected repositioning candidates using diabetes type 1 as an example.
  
  
• Predicting drug-target interactions through integrative analysis of chemogenetic assays in yeast.
  Heiskanen, Marja A and Aittokallio, Tero
  Molecular bioSystems, 2013, 9(4), 768-779
  PMID: 23420501     doi: 10.1039/c3mb25591c
  
   
  Chemical-genomic and genetic interaction profiling approaches are widely used to study mechanisms of drug action and resistance. However, there exist a number of scoring algorithms customized to different experimental assays, the relative performance of which remains poorly understood, especially with respect to different types of chemogenetic assays. Using yeast Saccharomyces cerevisiae as a test bed, we carried out a systematic evaluation among the main drug target analysis approaches in terms of predicting global drug-target interaction networks. We found drastic differences in their performance across different chemical-genomic assay types, such as those based on heterozygous and homozygous diploid or haploid deletion mutant libraries. Moreover, a relatively small overlap in the predicted targets was observed between those approaches that use either chemical-genomic screening alone or combined with genetic interaction profiling. A rank-based integration of the complementary scoring approaches led to improved overall performance, demonstrating that genetic interaction profiling provides added information on drug target prediction. Optimal performance was achieved when focusing specifically on the negative tail of the genetic interactions, suggesting that combining synthetic lethal interactions with chemical-genetic interactions provides highest information on drug-target interactions. A network view of rapamycin-interacting genes, pathways and complexes was used as an example to demonstrate the benefits of such integrated and optimized analysis of chemogenetic assays in yeast.
  
  
• Structure-Based Fragment Screening Is Demonstrated To Be a Practical Lead Discovery Method for a Representative G-Protein-Coupled Receptor
  Stevens, Benjamin D
  Journal of medicinal chemistry, 2013
  PMID: 23614494    
  
   
  
  
• Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments.
  Madhavi Sastry, G and Adzhigirey, Matvey and Day, Tyler and Annabhimoju, Ramakrishna and Sherman, Woody
  Journal of computer-aided molecular design, 2013, 27(3), 221-234
  PMID: 23579614     doi: 10.1007/s10822-013-9644-8
  
   
  Structure-based virtual screening plays an important role in drug discovery and complements other screening approaches. In general, protein crystal structures are prepared prior to docking in order to add hydrogen atoms, optimize hydrogen bonds, remove atomic clashes, and perform other operations that are not part of the x-ray crystal structure refinement process. In addition, ligands must be prepared to create 3-dimensional geometries, assign proper bond orders, and generate accessible tautomer and ionization states prior to virtual screening. While the prerequisite for proper system preparation is generally accepted in the field, an extensive study of the preparation steps and their effect on virtual screening enrichments has not been performed. In this work, we systematically explore each of the steps involved in preparing a system for virtual screening. We first explore a large number of parameters using the Glide validation set of 36 crystal structures and 1,000 decoys. We then apply a subset of protocols to the DUD database. We show that database enrichment is improved with proper preparation and that neglecting certain steps of the preparation process produces a systematic degradation in enrichments, which can be large for some targets. We provide examples illustrating the structural changes introduced by the preparation that impact database enrichment. While the work presented here was performed with the Protein Preparation Wizard and Glide, the insights and guidance are expected to be generalizable to structure-based virtual screening with other docking methods.
  
  
• Fragment-Based Drug Discovery Using a Multidomain, Parallel MD-MM/PBSA Screening Protocol
  Zhu, Tian and Lee, Hyun and Lei, Hao and Jones, Christopher and Patel, Kavankumar and Johnson, Michael E and Hevener, Kirk E
  Journal of chemical information and modeling, 2013, 53(3), 560-572
  PMID: 23432621    
  
   
  We have developed a rigorous computational screening protocol to identify novel fragment-like inhibitors of N(5)-CAIR mutase (PurE), a key enzyme involved in de novo purine synthesis that represents a novel target for the design of antibacterial agents. This computational screening protocol utilizes molecular docking, graphics processing unit (GPU)-accelerated molecular dynamics, and Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) free energy estimations to investigate the binding modes and energies of fragments in the active sites of PurE. PurE is a functional octamer comprised of identical subunits. The octameric structure, with its eight active sites, provided a distinct advantage in these studies because, for a given simulation length, we were able to place eight separate fragment compounds in the active sites to increase the throughput of the MM/PBSA analysis. To validate this protocol, we have screened an in-house fragment library consisting of 352 compounds. The theoretical results were then compared with the results of two experimental fragment screens, Nuclear Magnetic Resonance (NMR) and Surface Plasmon Resonance (SPR) binding analyses. In these validation studies, the protocol was able to effectively identify the competitive binders that had been independently identified by experimental testing, suggesting the potential utility of this method for the identification of novel fragments for future development as PurE inhibitors.
  
  
• FTFlex: accounting for binding site flexibility to improve fragment-based identification of druggable hot spots.
  Grove, Laurie E and Hall, David R and Beglov, Dmitri and Vajda, Sandor and Kozakov, Dima
  Bioinformatics (Oxford, England), 2013, 29(9), 1218-1219
  PMID: 23476022     doi: 10.1093/bioinformatics/btt102
  
   
  Computational solvent mapping finds binding hot spots, determines their druggability and provides information for drug design. While mapping of a ligand-bound structure yields more accurate results, usually the apo structure serves as the starting point in design. The FTFlex algorithm, implemented as a server, can modify an apo structure to yield mapping results that are similar to those of the respective bound structure. Thus, FTFlex is an extension of our FTMap server, which only considers rigid structures. FTFlex identifies flexible residues within the binding site and determines alternative conformations using a rotamer library. In cases where the mapping results of the apo structure were in poor agreement with those of the bound structure, FTFlex was able to yield a modified apo structure, which lead to improved FTMap results. In cases where the mapping results of the apo and bound structures were in good agreement, no new structure was predicted. AVAILABILITY: FTFlex is freely available as a web-based server at http://ftflex.bu.edu/. CONTACT: vajda@bu.edu or midas@bu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
  
  
• Characterizing Binding of Small Molecules. II. Evaluating the Potency of Small Molecules to Combat Resistance Based on Docking Structures
  Ding, Bo and Li, Nan and Wang, Wei
  Journal of chemical information and modeling, 2013
  PMID: 23570305    
  
   
  Drug resistance severely erodes the efficacy of therapeutic treatments for many diseases. Assessing the potency of a drug lead to combat resistance is no doubt critical for designing new drugs or new therapeutic combinations. Virtual screening is often the first step in drug discovery and a challenging problem is to accurately predict the resistant profile of an inhibitor based on the docking structures. Using a well studied system HIV-1 protease, we have illustrated the success of a computational method called MIEC-SVM on tackling this problem. We computed molecular interaction energy components (MIECs) between the ligand and the protease residues to characterize the docking poses, which were input to support vector machine (SVM) to distinguish resistant from nonresistant mutants. More importantly, the method is able to predict resistant profiles for new drugs based on the docking structures as indicated by its satisfactory performance in leave-one-drug-out and leave-drug/mutants-out tests. Therefore, the MIEC-SVM method can also facilitate designing effective therapeutic combinations by combining drugs with complementary resistant profiles.
  
  
• Accounting for Conformational Variability in Protein-Ligand Docking with NMR-Guided Rescoring.
  Skj{\ae}rven, Lars and Codutti, Luca and Angelini, Andrea and Grimaldi, Manuela and Latek, Dorota and Monecke, Peter and Dreyer, Matthias K and Carlomagno, Teresa
  Journal of the American Chemical Society, 2013, 135(15), 5819-5827
  PMID: 23565800     doi: 10.1021/ja4007468
  
   
  A key component to success in structure-based drug design is reliable information on protein-ligand interactions. Recent development in NMR techniques has accelerated this process by overcoming some of the limitations of X-ray crystallography and computational protein-ligand docking. In this work we present a new scoring protocol based on NMR-derived interligand INPHARMA NOEs to guide the selection of computationally generated docking modes. We demonstrate the performance in a range of scenarios, encompassing traditionally difficult cases such as docking to homology models and ligand dependent domain rearrangements. Ambiguities associated with sparse experimental information are lifted by searching a consensus solution based on simultaneously fitting multiple ligand pairs. This study provides a previously unexplored integration between molecular modeling and experimental data, in which interligand NOEs represent the key element in the rescoring algorithm. The presented protocol should be widely applicable for protein-ligand docking also in a different context from drug design and highlights the important role of NMR-based approaches to describe intermolecular ligand-receptor interactions.
  
  
• Automated docking with protein flexibility in the design of femtomolar "click chemistry" inhibitors of acetylcholinesterase.
  Morris, Garrett M and Green, Luke G and Radić, Zoran and Taylor, Palmer and Sharpless, K Barry and Olson, Arthur J and Grynszpan, Flavio
  Journal of chemical information and modeling, 2013, 53(4), 898-906
  PMID: 23451944     doi: 10.1021/ci300545a
  
   
  The use of computer-aided structure-based drug design prior to synthesis has proven to be generally valuable in suggesting improved binding analogues of existing ligands. (1) Here we describe the application of the program AutoDock (2) to the design of a focused library that was used in the "click chemistry in-situ" generation of the most potent noncovalent inhibitor of the native enzyme acetylcholinesterase (AChE) yet developed (Kd
  
  
• Optimization of molecular docking scores with support vector rank regression
  Wang, Wei and He, Wanlin and Zhou, Xi and Chen, Xin
  Proteins, 2013, n/a-n/a
  PMID: 23504920     doi: 10.1002/prot.24282
  
   
  This work introduces the support vector rank regression (SVRR) algorithm for the optimization of molecular docking scores. Seven original docking scores reported by two docking software were integrated by the SVRR algorithm. The resulting SVRR scores showed an average of 12.1% improvement (59.5% to 66.7%) in binding conformation prediction tests to rank the correctly computed conformation in the first place, along with 16.7% RMSD improvement (2.5414\AA} vs. 2.1162\AA}) for the top ranked conformations. In compound library screening tests, an average of 46.3% improvement (18.2% to 26.6%) was also observed to rank the correct ligand in the first place. Furthermore, it was shown that SVRR scores trained with different example datasets, using different training strategies, all exhibited exceedingly consistent accuracies, suggesting that the SVRR algorithm is highly robust and generalizable. In contrast, using the same training datasets, traditional support vector classification and regression algorithms failed to comparably improve the accuracy of library screening and conformation prediction. These results suggested that, with additional features to indicate the comparative fitness between computed binding conformations, the SVRR algorithm holds the potential to create a new category of more accurate integrative docking scores. Proteins 2013.
  
  
• APoc: large-scale identification of similar protein pockets.
  Gao, Mu and Skolnick, Jeffrey
  Bioinformatics (Oxford, England), 2013, 29(5), 597-604
  PMID: 23335017     doi: 10.1093/bioinformatics/btt024
  
   
  MOTIVATION:Most proteins interact with small-molecule ligands such as metabolites or drug compounds. Over the past several decades, many of these interactions have been captured in high-resolution atomic structures. From a geometric point of view, most interaction sites for grasping these small-molecule ligands, as revealed in these structures, form concave shapes, or 'pockets', on the protein's surface. An efficient method for comparing these pockets could greatly assist the classification of ligand-binding sites, prediction of protein molecular function and design of novel drug compounds.
  
  
• Adverse Drug Events: Database Construction and in Silico Prediction
  Cheng, Feixiong and Li, Weihua and Wang, Xichuan and Zhou, Yadi and Wu, Zengrui and Shen, Jie and Tang, Yun
  Journal of chemical information and modeling, 2013, 53(4), 744-752
  PMID: 23521697    
  
   
  Adverse drug events (ADEs) mean the harms associated with uses of given medications at normal dosages, which is crucial for a drug to be approved in clinic use or continue to stay in market. Many ADEs are not identified in clinical trials until the drug is approved for use in clinic, which results in adverse morbidity and mortality. To date millions of ADEs have been reported around the world. How to avoid or reduce ADEs is an important issue for drug discovery and development. Here, we reported a comprehensive database of adverse drug events (namely MetaADEDB), which included more than 52 thousands of drug-ADE associations among 3059 unique compounds (including 1330 drugs) and 13,200 ADE items by data integration and text mining. All compounds and ADEs were annotated with the most commonly used concepts defined in Medical Subject Headings (MeSH). Meanwhile, a computational method, namely phenotypic network inference model (PNIM), was developed for prediction of potential ADEs based on the database. The area under the receive operating characteristic curve (AUC) is more than 0.9 by 10-fold cross validation, while the AUC value was 0.912 for an external validation set extracted from the US-FDA Adverse Events Reporting System, which indicated that the prediction capability of the method was reliable. MetaADEDB is accessible free of charge at http://www.lmmd.org/online_services/metaadedb/. The database and the method provide us a useful tool to search for known side effects or predict potential side effects for a given drug or a compound.
  
  
• Docking Challenge: Protein Sampling and Molecular Docking Performance
  Elokely, Khaled M and Doerksen, Robert J
  Journal of chemical information and modeling, 2013
  PMID: 23530568    
  
   
  Computational tools are essential in the drug design process, especially in order to take advantage of the increasing numbers of solved X-ray and NMR protein-ligand structures. Nowadays, molecular docking methods are routinely used for prediction of protein-ligand interactions and to aid in selecting potent molecules as a part of virtual screening of large databases. The improvements and advances in computational capacity in the last decade have allowed for further developments in molecular docking algorithms to address more complicated aspects such as protein flexibility. The effects of incorporation of active site water molecules and implicit or explicit solvation of the binding site are other relevant issues to be addressed in the docking procedures. Using the right docking algorithm at the right stage of virtual screening is most important. We report a staged study to address the effects of various aspects of protein flexibility and inclusion of active site water molecules on docking effectiveness to retrieve (and to be able to predict) correct ligand poses and to rank docked ligands in relation to their biological activity, for CHK1, ERK2, LpxC and UPA. We generated multiple conformers for the ligand, and compared different docking algorithms that use a variety of approaches to protein flexibility, including rigid receptor, soft receptor, flexible side chains, induced-fit, and multiple structure algorithms. Docking accuracy varied from 1 to 84%, demonstrating that the choice of method is important.
  
  
• In Silico Fragment-Based Drug Discovery: Setup and Validation of a Fragment-to-Lead Computational Protocol Using S4MPLE
  Hoffer, Laurent and Renaud, Jean-Paul and Horvath, Dragos
  Journal of chemical information and modeling, 2013, 53(4), 836-851
  PMID: 23537132    
  
   
  This paper describes the use and validation of S4MPLE in Fragment-based Drug Design (FBDD) - a strategy to build drug-like ligands starting from small compounds called fragments. S4MPLE is a conformational sampling tool, based on a hybrid genetic algorithm, able to simulate one (conformer enumeration) or more molecules (docking). The goal of the current paper is to show that, due to the judicious design of genetic operators, S4MPLE may be used without any specific adaptation as an in silico FBDD tool. Such fragment-to-lead evolution involves either growing of one, or linking of several fragment-like binder(s). The native ability to specifically "dock" a substructure that is covalently anchored to its target (here, some prepositioned fragment formally part of the binding site) enables it to act like dedicated de novo builders and differentiates it from most classical docking tools, which may only cope with non-covalent interactions. Besides, S4MPLE may address growing/linking scenarios involving protein site flexibility, and it might also suggest "growth" moves by bridging the ligand to the site via water-mediated interactions, if H2O molecules are simply appended to the input files. Therefore, the only development overhead required to build a virtual fragment→ligand growing/linking strategy based on S4MPLE were two chemoinformatics programs, meant to provide a minimalistic management of the linker library. The first creates a duplicate-free library by fragmenting a compound database, whereas the second builds new compounds, attaching chemically compatible linkers to the starting fragments. S4MPLE is subsequently used to probe the optimal placement of the linkers within the binding site, with initial restraints on atoms from initial fragments, followed by an optimization of all kept poses, after restraint removal. Ranking is mainly based on two criteria: force-field potential energy and RMSD shifts of the original fragment moieties. This strategy was applied to several examples from the FBDD literature, with good results over several monitored criteria: ability to generate the optimized ligand (or close analogs), good ranking of analogs among decoy compounds, and accurate predictions of expected binding modes of reference ligands. Simulations included "classical" covalent growing/linking, more challenging ones involving binding site conformational changes and growth with optional recognition of putatively favorable water-mediated interactions.
  
  
• Automated Ligand- and Structure-Based Protocol for in Silico Prediction of Human Serum Albumin Binding
  Hall, Michelle Lynn and Jorgensen, William L and Whitehead, Lewis
  Journal of chemical information and modeling, 2013, 53(4), 907-922
  PMID: 23472823    
  
   
  Plasma protein binding has a profound impact on the pharmacokinetic and pharmacodynamic properties of many drug candidates and is thus an integral component of drug discovery. Nevertheless, extant methods to examine small-molecule interactions with plasma protein have various limitations, thus creating a need for alternative methods. Herein we present a comprehensive and cross-validated in silico workflow for the prediction of small-molecule binding to Human Serum Albumin (HSA), the most ubiquitous plasma protein. This protocol reliably predicts small-molecule interactions with HSA, including a binding affinity calculation using multiple linear regression methods, binding site prediction using a naive-Bayes classifier, and a three-dimensional binding pose using induced fit docking. Furthermore, this workflow is implemented in a portable and automated format that can be downloaded and used by other end users, either as is or with customization.
  
  
• Latest developments in molecular docking: 2010-2011 in review.
  Yuriev, Elizabeth and Ramsland, Paul A
  Journal of molecular recognition : JMR, 2013, 26(5), 215-239
  PMID: 23526775     doi: 10.1002/jmr.2266
  
   
  The aim of docking is to accurately predict the structure of a ligand within the constraints of a receptor binding site and to correctly estimate the strength of binding. We discuss, in detail, methodological developments that occurred in the docking field in 2010 and 2011, with a particular focus on the more difficult, and sometimes controversial, aspects of this promising computational discipline. The main developments in docking in this period, covered in this review, are receptor flexibility, solvation, fragment docking, postprocessing, docking into homology models, and docking comparisons. Several new, or at least newly invigorated, advances occurred in areas such as nonlinear scoring functions, using machine-learning approaches. This review is strongly focused on docking advances in the context of drug design, specifically in virtual screening and fragment-based drug design. Where appropriate, we refer readers to exemplar case studies. Copyright