Chemical space sampling by different scoring functions and crystal structures

Virtual screening has become a popular tool to identify novel leads in the early phases of drug discovery. A variety of docking and scoring methods used in virtual screening have been the subject of active research in an effort to gauge limitations and articulate best practices. However, how to best utilize different scoring functions and various crystal structures, when available, is not yet well understood. In this work we use multiple crystal structures of PI3 K-γ in both prospective and retrospective virtual screening experiments. Both Glide SP scoring and Prime MM-GBSA rescoring are utilized in the prospective and retrospective virtual screens, and consensus scoring is investigated in the retrospective virtual screening experiments. The results show that each of the different crystal structures that was used, samples a different chemical space, i.e. different chemotypes are prioritized by each structure. In addition, the different (re)scoring functions prioritize different chemotypes as well. Somewhat surprisingly, the Prime MM-GBSA scoring function generally gives lower enrichments than Glide SP. Finally we investigate the impact of different ligand preparation protocols on virtual screening enrichment factors. In summary, different crystal structures and different scoring functions are complementary to each other and allow for a wider variety of chemotypes to be considered for experimental follow-up.     

Evaluation of binary QSAR models derived from LUDI and MOE scoring functions for structure based virtual screening

In today's world of high-throughput in silico screening, the development of virtual screening methodologies to prioritize small molecules as new chemical entities (NCEs) for synthesis is of current interest. Among several approaches to virtual screening, structure-based virtual screening has been considered the most effective. However the problems associated with the ranking of potential solutions in terms of scoring functions remains one of the major bottlenecks in structure-based virtual screening technology. It has been suggested that scoring functions may be used as filters for distinguishing binders from nonbinders instead of accurately predicting their binding free energies. Subsequently, several improvements have been made in this area, which include the use of multiple rather than single scoring functions and application of either consensus or multivariate statistical methods or both to improve the discrimination between binders and nonbinders. In view of it, the discriminative ability (distinguishing binders from nonbinders) of binary QSAR models derived using LUDI and MOE scoring functions has been compared with the models derived by Jacobbsson et al. on five data sets viz. estrogen receptor alphamimics (ERalpha_mimics), estrogen receptor alphatoxins (ERalpha_toxins), matrix metalloprotease 3 inhibitors (MMP-3), factor Xa inhibitors (fXa), and acetylcholine esterase inhibitors (AChE). The overall analyses reveal that binary QSAR is comparable to the PLS discriminant analysis, rule-based, and Bayesian classification methods used by Jacobsson et al. Further the scoring functions implemented in LUDI and MOE can score a wide range of protein-ligand interactions and are comparable to the scoring functions implemented in ICM and Cscore. Thus the binary QSAR models derived using LUDI and MOE scoring functions may be useful as a preliminary screening layer in a multilayered virtual screening paradigm.     

Design of multi-binding-site inhibitors, ligand efficiency, and consensus screening of avian influenza H5N1 wild-type neuraminidase and of the oseltamivir-resistant H274Y variant

The binding sites of wild-type avian influenza A H5N1 neuraminidase, as well as those of the Tamiflu (oseltamivir)-resistant H274Y variant, were explored computationally to design inhibitors that target simultaneously several adjacent binding sites of the open conformation of the virus protein. The compounds with the best computed free energies of binding, in agreement by two docking methods, consensus scoring, and ligand efficiency values, suggest that mimicking a polysaccharide, beta-lactam, and other structures, including known drugs, could be routes for multibinding site inhibitor design. This new virtual screening method based on consensus scoring and ligand efficiency indices is introduced, which allows the combination of pharmacodynamic and pharmacokinetic properties into unique measures.     

How does consensus scoring work for virtual library screening? An idealized computer experiment

It has been reported recently that consensus scoring, which combines multiple scoring functions in binding affinity estimation, leads to higher hit-rates in virtual library screening studies. This method seems quite independent to the target receptor, the docking program, or even the scoring functions under investigation. Here we present an idealized computer experiment to explore how consensus scoring works. A hypothetical set of 5000 compounds is used to represent a chemical library under screening. The binding affinities of all its member compounds are assigned by mimicking a real situation. Based on the assumption that the error of a scoring function is a random number in a normal distribution, the predicted binding affinities were generated by adding such a random number to the "observed" binding affinities. The relationship between the hit-rates and the number of scoring functions employed in scoring was then investigated. The performance of several typical ranking strategies for a consensus scoring procedure was also explored. Our results demonstrate that consensus scoring outperforms any single scoring for a simple statistical reason: the mean value of repeated samplings tends to be closer to the true value. Our results also suggest that a moderate number of scoring functions, three or four, are sufficient for the purpose of consensus scoring. As for the ranking strategy, both the rank-by-number and the rank-by-rank strategy work more effectively than the rank-by-vote strategy.     

Consensus scoring with feature selection for structure-based virtual screening

The evaluation of ligand conformations is a crucial aspect of structure-based virtual screening, and scoring functions play significant roles in it. While consensus scoring (CS) generally improves enrichment by compensating for the deficiencies of each scoring function, the strategy of how individual scoring functions are selected remains a challenging task when few known active compounds are available. To address this problem, we propose feature selection-based consensus scoring (FSCS), which performs supervised feature selection with docked native ligand conformations to select complementary scoring functions. We evaluated the enrichments of five scoring functions (F-Score, D-Score, PMF, G-Score, and ChemScore), FSCS, and RCS (rank-by-rank consensus scoring) for four different target proteins: acetylcholine esterase (AChE), thrombin (thrombin), phosphodiesterase 5 (PDE5), and peroxisome proliferator-activated receptor gamma (PPARgamma). The results indicated that FSCS was able to select the complementary scoring functions and enhance ligand enrichments and that it outperformed RCS and the individual scoring functions for all target proteins. They also indicated that the performances of the single scoring functions were strongly dependent on the target protein. An especially favorable result with implications for practical drug screening is that FSCS performs well even if only one 3D structure of the protein-ligand complex is known. Moreover, we found that one can infer which scoring functions significantly enrich active compounds by using feature selection before actual docking and that the selected scoring functions are complementary.     

Impact of scoring functions on enrichment in docking-based virtual screening: an application study on renin inhibitors

The docking program LigandFit/Cerius(2) has been used to perform shape-based virtual screening of databases against the aspartic protease renin, a target of determined three-dimensional structure. The protein structure was used in the induced fit binding conformation that occurs when renin is bound to the highly active renin inhibitor 1 (IC(50) = 2 nM). The scoring was calculated using several different scoring functions in order to get insight into the predictability of the magnitude of binding interactions. A database of 1000 diverse and druglike compounds, comprised of 990 members of a virtual database generated by using the iLib diverse software and 10 known active renin inhibitors, was docked flexibly and scored to determine appropriate scoring functions. All seven scoring functions used (LigScore1, LigScore2, PLP1, PLP2, JAIN, PMF, LUDI) were able to retrieve at least 50% of the active compounds within the first 20% (200 molecules) of the entire test database. A hit rate of 90% in the top 1.4% resulted using the quadruple consensus scoring of LigScore2, PLP1, PLP2, and JAIN. Additionally, a focused database was created with the iLib diverse software and used for the same procedure as the test database. Docking and scoring of the 990 focused compounds and the 10 known actives were performed. A hit rate of 100% in the top 8.4% resulted with use of the triple consensus scoring of PLP1, PLP2, and PMF. As expected, a ranking of the known active compounds within the focused database compared to the test database was observed. Adequate virtual screening conditions were derived empirically. They can be used for proximate docking and scoring application of compounds with putative renin inhibiting potency.     

New scoring functions for virtual screening from molecular dynamics simulations with a quantum-refined force-field (QRFF-MD). Application to cyclin-dependent kinase 2

A recently introduced new methodology based on ultrashort (50-100 ps) molecular dynamics simulations with a quantum-refined force-field (QRFF-MD) is here evaluated in its ability both to predict protein-ligand binding affinities and to discriminate active compounds from inactive ones. Physically based scoring functions are derived from this approach, and their performance is compared to that of several standard knowledge-based scoring functions. About 40 inhibitors of cyclin-dependent kinase 2 (CDK2) representing a broad chemical diversity were considered. The QRFF-MD method achieves a correlation coefficient, R(2), of 0.55, which is significantly better than that obtained by a number of traditional approaches in virtual screening but only slightly better than that obtained by consensus scoring (R(2) = 0.50). Compounds from the Available Chemical Directory, along with the known active compounds, were docked into the ATP binding site of CDK2 using the program Glide, and the 650 ligands from the top scored poses were considered for a QRFF-MD analysis. Combined with structural information extracted from the simulations, the QRFF-MD methodology results in similar enrichment of known actives compared to consensus scoring. Moreover, a new scoring function is introduced that combines a QRFF-MD based scoring function with consensus scoring, which results in substantial improvement on the enrichment profile.     

Supervised consensus scoring for docking and virtual screening

Docking programs are widely used to discover novel ligands efficiently and can predict protein-ligand complex structures with reasonable accuracy and speed. However, there is an emerging demand for better performance from the scoring methods. Consensus scoring (CS) methods improve the performance by compensating for the deficiencies of each scoring function. However, conventional CS and existing scoring functions have the same problems, such as a lack of protein flexibility, inadequate treatment of salvation, and the simplistic nature of the energy function used. Although there are many problems in current scoring functions, we focus our attention on the incorporation of unbound ligand conformations. To address this problem, we propose supervised consensus scoring (SCS), which takes into account protein-ligand binding process using unbound ligand conformations with supervised learning. An evaluation of docking accuracy for 100 diverse protein-ligand complexes shows that SCS outperforms both CS and 11 scoring functions (PLP, F-Score, LigScore, DrugScore, LUDI, X-Score, AutoDock, PMF, G-Score, ChemScore, and D-score). The success rates of SCS range from 89% to 91% in the range of rmsd < 2 A, while those of CS range from 80% to 85%, and those of the scoring functions range from 26% to 76%. Moreover, we also introduce a method for judging whether a compound is active or inactive with the appropriate criterion for virtual screening. SCS performs quite well in docking accuracy and is presumably useful for screening large-scale compound databases before predicting binding affinity.     

Consensus scoring criteria for improving enrichment in virtual screening

MOTIVATION: Virtual screening of molecular compound libraries is a potentially powerful and inexpensive method for the discovery of novel lead compounds for drug development. The major weakness of virtual screening-the inability to consistently identify true positives (leads)-is likely due to our incomplete understanding of the chemistry involved in ligand binding and the subsequently imprecise scoring algorithms. It has been demonstrated that combining multiple scoring functions (consensus scoring) improves the enrichment of true positives. Previous efforts at consensus scoring have largely focused on empirical results, but they have yet to provide a theoretical analysis that gives insight into real features of combinations and data fusion for virtual screening. RESULTS: We demonstrate that combining multiple scoring functions improves the enrichment of true positives only if (a) each of the individual scoring functions has relatively high performance and (b) the individual scoring functions are distinctive. Notably, these two prediction variables are previously established criteria for the performance of data fusion approaches using either rank or score combinations. This work, thus, establishes a potential theoretical basis for the probable success of data fusion approaches to improve yields in in silico screening experiments. Furthermore, it is similarly established that the second criterion (b) can, in at least some cases, be functionally defined as the area between the rank versus score plots generated by the two (or more) algorithms. Because rank-score plots are independent of the performance of the individual scoring function, this establishes a second theoretically defined approach to determining the likely success of combining data from different predictive algorithms. This approach is, thus, useful in practical settings in the virtual screening process when the performance of at least two individual scoring functions (such as in criterion a) can be estimated as having a high likelihood of having high performance, even if no training sets are available. We provide initial validation of this theoretical approach using data from five scoring systems with two evolutionary docking algorithms on four targets, thymidine kinase, human dihydrofolate reductase, and estrogen receptors of antagonists and agonists. Our procedure is computationally efficient, able to adapt to different situations, and scalable to a large number of compounds as well as to a greater number of combinations. Results of the experiment show a fairly significant improvement (vs single algorithms) in several measures of scoring quality, specifically "goodness-of-hit" scores, false positive rates, and "enrichment". This approach (available online at http://gemdock.life. nctu.edu.tw/dock/download.php) has practical utility for cases where the basic tools are known or believed to be generally applicable, but where specific training sets are absent.     

Pose prediction and virtual screening performance of GOLD scoring functions in a standardized test

The performance of all four GOLD scoring functions has been evaluated for pose prediction and virtual screening under the standardized conditions of the comparative docking and scoring experiment reported in this Edition. Excellent pose prediction and good virtual screening performance was demonstrated using unmodified protein models and default parameter settings. The best performing scoring function for both pose prediction and virtual screening was demonstrated to be the recently introduced scoring function ChemPLP. We conclude that existing docking programs already perform close to optimally in the cognate pose prediction experiments currently carried out and that more stringent pose prediction tests should be used in the future. These should employ cross-docking sets. Evaluation of virtual screening performance remains problematic and much remains to be done to improve the usefulness of publically available active and decoy sets for virtual screening. Finally we suggest that, for certain target/scoring function combinations, good enrichment may sometimes be a consequence of 2D property recognition rather than a modelling of the correct 3D interactions.     

Evaluation of docking calculations on X-ray structures using CONSENSUS-DOCK

We are participating in the challenge of identifying active compounds for target proteins using structure-based virtual screening (SBVS). We use an in-house customized docking program, CONSENSUS-DOCK, which is a customized version of the DOCK4 program in which three scoring functions (DOCK4, FlexX and PMF) and consensus scoring have been implemented. This paper compares the docking calculation results obtained using CONSENSUS-DOCK and DOCK4, and demonstrates that CONSENSUS-DOCK produces better results than DOCK4 for major X-ray structures obtained from the Protein Data Bank (PDB).     

Evaluation and application of multiple scoring functions for a virtual screening experiment

In order to identify novel chemical classes of factor Xa inhibitors, five scoring functions (FlexX, DOCK, GOLD, ChemScore and PMF) were engaged to evaluate the multiple docking poses generated by FlexX. The compound collection was composed of confirmed potent factor Xa inhibitors and a subset of the LeadQuest screening compound library. Except for PMF the other four scoring functions succeeded in reproducing the crystal complex (PDB code: 1FAX). During virtual screening the highest hit rate (80%) was demonstrated by FlexX at an energy cutoff of -40 kJ/mol, which is about 40-fold over random screening (2.06%). Limited results suggest that presenting more poses of a single molecule to the scoring functions could deteriorate their enrichment factors. A series of promising scaffolds with favorable binding scores was retrieved from LeadQuest. Consensus scoring by pair-wise intersection failed to enrich the hit rate yielded by single scorings (i.e. FlexX). We note that reported successes of consensus scoring in hit rate enrichment could be artificial because their comparisons were based on a selected subset of single scoring and a markedly reduced subset of double or triple scoring. The findings presented in this report are based upon a single biological system and support further studies.     

A D3R prospective evaluation of machine learning for protein-ligand scoring

We assess the performance of several machine learning-based scoring methods at protein-ligand pose prediction, virtual screening, and binding affinity prediction. The methods and the manner in which they were trained make them sufficiently diverse to evaluate the utility of various strategies for training set curation and binding pose generation, but they share a novel approach to classification in the context of protein-ligand scoring. Rather than explicitly using structural data such as affinity values or information extracted from crystal binding poses for training, we instead exploit the abundance of data available from high-throughput screening to approach the problem as one of discriminating binders from non-binders. We evaluate the performance of our various scoring methods in the 2015 D3R Grand Challenge and find that although the merits of some features of our approach remain inconclusive, our scoring methods performed comparably to a state-of-the-art scoring function that was fit to binding affinity data.     

Pharmacophore alignment search tool: influence of scoring systems on text-based similarity searching

The text-based similarity searching method Pharmacophore Alignment Search Tool is grounded on pairwise comparisons of potential pharmacophoric points between a query and screening compounds. The underlying scoring matrix is of critical importance for successful virtual screening and hit retrieval from large compound libraries. Here, we compare three conceptually different computational methods for systematic deduction of scoring matrices: assignment-based, alignment-based, and stochastic optimization. All three methods resulted in optimized pharmacophore scoring matrices with significantly superior retrospective performance in comparison with simplistic scoring schemes. Computer-generated similarity matrices of pharmacophoric features turned out to agree well with a manually constructed matrix. We introduce the concept of position-specific scoring to text-based similarity searching so that knowledge about specific ligand-receptor binding patterns can be included and demonstrate its benefit for hit retrieval. The approach was also used for automated pharmacophore elucidation in agonists of peroxisome proliferator activated receptor gamma, successfully identifying key interactions for receptor activation.     

Identification of a minimal subset of receptor conformations for improved multiple conformation docking and two-step scoring

Docking and scoring are critical issues in virtual drug screening methods. Fast and reliable methods are required for the prediction of binding affinity especially when applied to a large library of compounds. The implementation of receptor flexibility and refinement of scoring functions for this purpose are extremely challenging in terms of computational speed. Here we propose a knowledge-based multiple-conformation docking method that efficiently accommodates receptor flexibility thus permitting reliable virtual screening of large compound libraries. Starting with a small number of active compounds, a preliminary docking operation is conducted on a large ensemble of receptor conformations to select the minimal subset of receptor conformations that provides a strong correlation between the experimental binding affinity (e.g., Ki, IC50) and the docking score. Only this subset is used for subsequent multiple-conformation docking of the entire data set of library (test) compounds. In conjunction with the multiple-conformation docking procedure, a two-step scoring scheme is employed by which the optimal scoring geometries obtained from the multiple-conformation docking are re-scored by a molecular mechanics energy function including desolvation terms. To demonstrate the feasibility of this approach, we applied this integrated approach to the estrogen receptor alpha (ERalpha) system for which published binding affinity data were available for a series of structurally diverse chemicals. The statistical correlation between docking scores and experimental values was significantly improved from those of single-conformation dockings. This approach led to substantial enrichment of the virtual screening conducted on mixtures of active and inactive ERalpha compounds.     

Structure-based virtual screening with supervised consensus scoring: evaluation of pose prediction and enrichment factors

Since the evaluation of ligand conformations is a crucial aspect of structure-based virtual screening, scoring functions play significant roles in it. However, it is known that a scoring function does not always work well for all target proteins. When one cannot know which scoring function works best against a target protein a priori, there is no standard scoring method to know it even if 3D structure of a target protein-ligand complex is available. Therefore, development of the method to achieve high enrichments from given scoring functions and 3D structure of protein-ligand complex is a crucial and challenging task. To address this problem, we applied SCS (supervised consensus scoring), which employs a rough linear correlation between the binding free energy and the root-mean-square deviation (rmsd) of a native ligand conformations and incorporates protein-ligand binding process with docked ligand conformations using supervised learning, to virtual screening. We evaluated both the docking poses and enrichments of SCS and five scoring functions (F-Score, G-Score, D-Score, ChemScore, and PMF) for three different target proteins: thymidine kinase (TK), thrombin (thrombin), and peroxisome proliferator-activated receptor gamma (PPARgamma). Our enrichment studies show that SCS is competitive or superior to a best single scoring function at the top ranks of screened database. We found that the enrichments of SCS could be limited by a best scoring function, because SCS is obtained on the basis of the five individual scoring functions. Therefore, it is concluded that SCS works very successfully from our results. Moreover, from docking pose analysis, we revealed the connection between enrichment and average centroid distance of top-scored docking poses. Since SCS requires only one 3D structure of protein-ligand complex, SCS will be useful for identifying new ligands.     

Virtual Screening with Gnina 1.0

Virtual screening—predicting which compounds within a specified compound library bind to a target molecule, typically a protein—is a fundamental task in the field of drug discovery. Doing virtual screening well provides tangible practical benefits, including reduced drug development costs, faster time to therapeutic viability, and fewer unforeseen side effects. As with most applied computational tasks, the algorithms currently used to perform virtual screening feature inherent tradeoffs between speed and accuracy. Furthermore, even theoretically rigorous, computationally intensive methods may fail to account for important effects relevant to whether a given compound will ultimately be usable as a drug. Here we investigate the virtual screening performance of the recently released Gnina molecular docking software, which uses deep convolutional networks to score protein-ligand structures. We find, on average, that Gnina outperforms conventional empirical scoring. The default scoring in Gnina outperforms the empirical AutoDock Vina scoring function on 89 of the 117 targets of the DUD-E and LIT-PCBA virtual screening benchmarks with a median 1% early enrichment factor that is more than twice that of Vina. However, we also find that issues of bias linger in these sets, even when not used directly to train models, and this bias obfuscates to what extent machine learning models are achieving their performance through a sophisticated interpretation of molecular interactions versus fitting to non-informative simplistic property distributions.     

Homology model-based virtual screening for GPCR ligands using docking and target-biased scoring

The current study investigates the combination of two recently reported techniques for the improvement of homology model-based virtual screening for G-protein coupled receptor (GPCR) ligands. First, ligand-supported homology modeling was used to generate receptor models that were in agreement with mutagenesis data and structure-activity relationship information of the ligands. Second, interaction patterns from known ligands to the receptor were applied for scoring and rank ordering compounds from a virtual library using ligand-receptor interaction fingerprint-based similarity (IFS). Our approach was evaluated in retrospective virtual screening experiments for antagonists of the metabotropic glutamate receptor (mGluR) subtype 5. The results of our approach were compared to the results obtained by conventional scoring functions (Dock-Score, PMF-Score, Gold-Score, ChemScore, and FlexX-Score). The IFS lead to significantly higher enrichment rates, relative to the competing scoring functions. Though using a target-biased scoring approach, the results were not biased toward the chemical classes of the reference structures. Our results indicate that the presented approach has the potential to serve as a general setup for successful structure-based GPCR virtual screening.