Calculations of protein-ligand binding entropy of relative and overall molecular motions

In the context of virtual database screening, calculations of protein-ligand binding entropy of relative and overall molecular motions are challenging, owing to the inherent structural complexity of the ligand binding well in the energy landscape of protein-ligand interactions and computing time limitations. We describe a fast statistical thermodynamic method for estimation the binding entropy to address the challenges. The method is based on the integration of the configurational integral over clusters obtained from multiple docked positions. We apply the method in conjunction with 11 popular scoring functions (AutoDock, ChemScore, DrugScore, D-Score, F-Score, G-Score, LigScore, LUDI, PLP, PMF, X-Score) to evaluate the binding entropy of 100 protein-ligand complexes. The averaged values of binding entropy contribution vary from 6.2 to 9.1 kcal/mol, showing good agreement with literature. We calculate positional sizes and the angular volume of the native ligand wells. The averaged geometric mean of positional sizes in principal directions varies from 0.8 to 1.4 A. The calculated range of angular volumes is 3.3-11.8 rad(2). Then we demonstrate that the averaged six-dimensional volume of the native well is larger than the volume of the most populated non-native well in energy landscapes described by all of 11 scoring functions.     

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.     

Comparison of consensus scoring strategies for evaluating computational models of protein-ligand complexes

Here, the comparisons of performance of nine consensus scoring strategies, in which multiple scoring functions were used simultaneously to evaluate candidate structures for a protein-ligand complex, in combination with nine scoring functions (FlexX score, GOLD score, PMF score, DOCK score, ChemScore, DrugScore, PLP, ScreenScore, and X-Score), were carried out. The systematic naming of consensus scoring strategies was also proposed. Our results demonstrate that choosing the most appropriate type of consensus score is essential for model selection in computational docking; although the vote-by-number strategy was an effective selection method, the number-by-number and rank-by-number strategies were more appropriate when computational tractability was taken into account. By incorporating these consensus scores into the FlexX program, reasonable complex models can be obtained more efficiently than those selected by independent FlexX scores. These strategies might also improve the scoring of other docking programs, and more-effective structure-based drug design should result from these improvements.     

Role of binding entropy in the refinement of protein-ligand docking predictions: analysis based on the use of 11 scoring functions

We present results of testing the ability of eleven popular scoring functions to predict native docked positions using a recently developed method (Ruvinsky and Kozintsev, J Comput Chem 2005, 26, 1089) for estimation the entropy contributions of relative motions to protein-ligand binding affinity. The method is based on the integration of the configurational integral over clusters obtained from multiple docked positions. We use a test set of 100 PDB protein-ligand complexes and ensembles of 101 docked positions generated by (Wang et al. J Med Chem 2003, 46, 2287) for each ligand in the test set. To test the suggested method we compared the averaged root-mean square deviations (RMSD) of the top-scored ligand docked positions, accounting and not accounting for entropy contributions, relative to the experimentally determined positions. We demonstrate that the method increases docking accuracy by 10-21% when used in conjunction with the AutoDock scoring function, by 2-25% with G-Score, by 7-41% with D-Score, by 0-8% with LigScore, by 1-6% with PLP, by 0-12% with LUDI, by 2-8% with F-Score, by 7-29% with ChemScore, by 0-9% with X-Score, by 2-19% with PMF, and by 1-7% with DrugScore. We also compared the performance of the suggested method with the method based on ranking by cluster occupancy only. We analyze how the choice of a clustering-RMSD and a low bound of dense clusters impacts on docking accuracy of the scoring methods. We derive optimal intervals of the clustering-RMSD for 11 scoring functions.     

DrugScoreRNA--knowledge-based scoring function to predict RNA-ligand interactions

There is growing interest in RNA as a drug target due to its widespread involvement in biological processes. To exploit the power of structure-based drug-design approaches, novel scoring and docking tools need to be developed that can efficiently and reliably predict binding modes and binding affinities of RNA ligands. We report for the first time the development of a knowledge-based scoring function to predict RNA-ligand interactions (DrugScoreRNA). Based on the formalism of the DrugScore approach, distance-dependent pair potentials are derived from 670 crystallographically determined nucleic acid-ligand and -protein complexes. These potentials display quantitative differences compared to those of DrugScore (derived from protein-ligand complexes) and DrugScoreCSD (derived from small-molecule crystal data). When used as an objective function for docking 31 RNA-ligand complexes, DrugScoreRNA generates "good" binding geometries (rmsd (root mean-square deviation) < 2 A) in 42% of all cases on the first scoring rank. This is an improvement of 44% to 120% when compared to DrugScore, DrugScoreCSD, and an RNA-adapted AutoDock scoring function. Encouragingly, good docking results are also obtained for a subset of 20 NMR structures not contained in the knowledge-base to derive the potentials. This clearly demonstrates the robustness of the potentials. Binding free energy landscapes generated by DrugScoreRNA show a pronounced funnel shape in almost 3/4 of all cases, indicating the reduced steepness of the knowledge-based potentials. Docking with DrugScoreRNA can thus be expected to converge fast to the global minimum. Finally, binding affinities were predicted for 15 RNA-ligand complexes with DrugScoreRNA. A fair correlation between experimental and computed values is found (RS = 0.61), which suffices to distinguish weak from strong binders, as is required in virtual screening applications. DrugScoreRNA again shows superior predictive power when compared to DrugScore, DrugScoreCSD, and an RNA-adapted AutoDock scoring function.     

Information theory-based scoring function for the structure-based prediction of protein-ligand binding affinity

The development and validation of a new knowledge based scoring function (SIScoreJE) to predict binding energy between proteins and ligands is presented. SIScoreJE efficiently predicts the binding energy between a small molecule and its protein receptor. Protein-ligand atomic contact information was derived from a Non-Redundant Data set (NRD) of over 3000 X-ray crystal structures of protein-ligand complexes. This information was classified for individual "atom contact pairs" (ACP) which is used to calculate the atomic contact preferences. In addition to the two schemes generated in this study we have assessed a number of other common atom-type classification schemes. The preferences were calculated using an information theoretic relationship of joint entropy. Among 18 different atom-type classification schemes "ScoreJE Atom Type set2" (SATs2) was found to be the most suitable for our approach. To test the sensitivity of the method to the inclusion of solvent, Single-body Solvation Potentials (SSP) were also derived from the atomic contacts between the protein atom types and water molecules modeled using AQUARIUS2. Validation was carried out using an evaluation data set of 100 protein-ligand complexes with known binding energies to test the ability of the scoring functions to reproduce known binding affinities. In summary, it was found that a combined SSP/ScoreJE (SIScoreJE) performed significantly better than ScoreJE alone, and SIScoreJE and ScoreJE performed better than GOLD::GoldScore, GOLD::ChemScore, and XScore.     

Statistical potential for modeling and ranking of protein-ligand interactions

Applications in structural biology and medicinal chemistry require protein-ligand scoring functions for two distinct tasks: (i) ranking different poses of a small molecule in a protein binding site and (ii) ranking different small molecules by their complementarity to a protein site. Using probability theory, we developed two atomic distance-dependent statistical scoring functions: PoseScore was optimized for recognizing native binding geometries of ligands from other poses and RankScore was optimized for distinguishing ligands from nonbinding molecules. Both scores are based on a set of 8,885 crystallographic structures of protein-ligand complexes but differ in the values of three key parameters. Factors influencing the accuracy of scoring were investigated, including the maximal atomic distance and non-native ligand geometries used for scoring, as well as the use of protein models instead of crystallographic structures for training and testing the scoring function. For the test set of 19 targets, RankScore improved the ligand enrichment (logAUC) and early enrichment (EF(1)) scores computed by DOCK 3.6 for 13 and 14 targets, respectively. In addition, RankScore performed better at rescoring than each of seven other scoring functions tested. Accepting both the crystal structure and decoy geometries with all-atom root-mean-square errors of up to 2 ? from the crystal structure as correct binding poses, PoseScore gave the best score to a correct binding pose among 100 decoys for 88% of all cases in a benchmark set containing 100 protein-ligand complexes. PoseScore accuracy is comparable to that of DrugScore(CSD) and ITScore/SE and superior to 12 other tested scoring functions. Therefore, RankScore can facilitate ligand discovery, by ranking complexes of the target with different small molecules; PoseScore can be used for protein-ligand complex structure prediction, by ranking different conformations of a given protein-ligand pair. The statistical potentials are available through the Integrative Modeling Platform (IMP) software package (http://salilab.org/imp) and the LigScore Web server (http://salilab.org/ligscore/).     

Scoring confidence index: statistical evaluation of ligand binding mode predictions

Protein-ligand docking programs can generate a large number of possible binding orientations for each ligand candidate. The challenge is to identify the orientations closest to the native binding mode using a scoring method. Many different scoring functions have been developed for protein-ligand scoring, but their performance on binding mode prediction is often target-dependent. In this study, a statistical approach was employed to provide a confidence measure of scoring performance in finding close to the correct docked ligand orientations. It exploits the fact that the scores provided by an adequately performing scoring function generally improve as the ligand binding modes get closer to the correct native orientation. For such cases, the correlation coefficient of scores versus distances is expected to be highest when the most native-like orientation is used as a reference. This correlation coefficient, called the correlation-based score (CBScore), was used as an indicator of how far the docked pose was from the native orientation. The correlation between the original scores and CBScores as well as the range of CBScores were found to be good measures of scoring performance. They were combined into a single quantity, called the scoring confidence index. High values of the scoring confidence index were indicative of pronounced and relatively smooth binding energy landscapes with easily discernable global minima, resulting in reliable binding mode predictions. Low values of this index reflected rugged energy landscapes making the prediction of the correct binding mode very difficult and often unreliable. The diagnostic ability of the scoring confidence index was tested on a non-redundant set of 50 protein-ligand complexes scored with three commonly employed scoring functions: AffiScore, DrugScore and X-Score. Binding mode predictions were found to be three times more reliable for complexes with scoring confidence indices in the upper half than for cases with values in the lower half of the resulting range of 0–1.6. This new confidence measure of scoring performance is expected to be a valuable tool for virtual screening applications. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Ligand Identification Scoring Algorithm (LISA)

A central problem in de novo drug design is determining the binding affinity of a ligand with a receptor. A new scoring algorithm is presented that estimates the binding affinity of a protein-ligand complex given a three-dimensional structure. The method, LISA (Ligand Identification Scoring Algorithm), uses an empirical scoring function to describe the binding free energy. Interaction terms have been designed to account for van der Waals (VDW) contacts, hydrogen bonding, desolvation effects, and metal chelation to model the dissociation equilibrium constants using a linear model. Atom types have been introduced to differentiate the parameters for VDW, H-bonding interactions, and metal chelation between different atom pairs. A training set of 492 protein-ligand complexes was selected for the fitting process. Different test sets have been examined to evaluate its ability to predict experimentally measured binding affinities. By comparing with other well-known scoring functions, the results show that LISA has advantages over many existing scoring functions in simulating protein-ligand binding affinity, especially metalloprotein-ligand binding affinity. Artificial Neural Network (ANN) was also used in order to demonstrate that the energy terms in LISA are well designed and do not require extra cross terms.     

A quantum mechanics-based scoring function: study of zinc ion-mediated ligand binding

In this communication, we report the development of a novel quantum mechanics-based scoring function to predict free energy of ligand binding in the zinc metalloenzymes carbonic anhydrase (CA) and carboxypeptidase A (CPA). In particular, the AM1 method is used in conjunction with solvation modeling to predict the relative binding affinities of 18 CA and 5 CPA inhibitors. The effect of metal-ligand charge transfer is also discussed and shown to be different in CPA and CA, providing a further challenge to computing metalloenzyme binding affinities.     

Knowledge-based scoring functions in drug design: 2. Can the knowledge base be enriched?

Fast and accurate predicting of the binding affinities of large sets of diverse protein?ligand complexes is an important, yet extremely challenging, task in drug discovery. The development of knowledge-based scoring functions exploiting structural information of known protein?ligand complexes represents a valuable contribution to such a computational prediction. In this study, we report a scoring function named IPMF that integrates additional experimental binding affinity information into the extracted potentials, on the assumption that a scoring function with the "enriched" knowledge base may achieve increased accuracy in binding affinity prediction. In our approach, the functions and atom types of PMF04 were inherited to implicitly capture binding effects that are hard to model explicitly, and a novel iteration device was designed to gradually tailor the initial potentials. We evaluated the performance of the resultant IPMF with a diverse set of 219 protein-ligand complexes and compared it with seven scoring functions commonly used in computer-aided drug design, including GLIDE, AutoDock4, VINA, PLP, LUDI, PMF, and PMF04. While the IPMF is only moderately successful in ranking native or near native conformations, it yields the lowest mean error of 1.41 log K(i)/K(d) units from measured inhibition affinities and the highest Pearson's correlation coefficient of R(p)2 0.40 for the test set. These results corroborate our initial supposition about the role of "enriched" knowledge base. With the rapid growing volume of high-quality structural and interaction data in the public domain, this work marks a positive step toward improving the accuracy of knowledge-based scoring functions in binding affinity prediction.     

An iterative knowledge-based scoring function to predict protein-ligand interactions: II. Validation of the scoring function

We have developed an iterative knowledge-based scoring function (ITScore) to describe protein-ligand interactions. Here, we assess ITScore through extensive tests on native structure identification, binding affinity prediction, and virtual database screening. Specifically, ITScore was first applied to a test set of 100 protein-ligand complexes constructed by Wang et al. (J Med Chem 2003, 46, 2287), and compared with 14 other scoring functions. The results show that ITScore yielded a high success rate of 82% on identifying native-like binding modes under the criterion of rmsd < or = 2 A for each top-ranked ligand conformation. The success rate increased to 98% if the top five conformations were considered for each ligand. In the case of binding affinity prediction, ITScore also obtained a good correlation for this test set (R = 0.65). Next, ITScore was used to predict binding affinities of a second diverse test set of 77 protein-ligand complexes prepared by Muegge and Martin (J Med Chem 1999, 42, 791), and compared with four other widely used knowledge-based scoring functions. ITScore yielded a high correlation of R2 = 0.65 (or R = 0.81) in the affinity prediction. Finally, enrichment tests were performed with ITScore against four target proteins using the compound databases constructed by Jacobsson et al. (J Med Chem 2003, 46, 5781). The results were compared with those of eight other scoring functions. ITScore yielded high enrichments in all four database screening tests. ITScore can be easily combined with the existing docking programs for the use of structure-based drug design.     

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.     

Predicting protein-ligand binding affinities: a low scoring game?

We have investigated the performance of five well known scoring functions in predicting the binding affinities of a diverse set of 205 protein-ligand complexes with known experimental binding constants, and also on subsets of mutually similar complexes. We have found that the overall performance of the scoring functions on the diverse set is disappointing, with none of the functions achieving r(2) values above 0.32 on the whole dataset. Performance on the subsets was mixed, with four of the five functions predicting fairly well the binding affinities of 35 proteinases, but none of the functions producing any useful correlation on a set of 38 aspartic proteinases. We consider two algorithms for producing consensus scoring functions, one based on a linear combination of scores from the five individual functions and the other on averaging the rankings produced by the five functions. We find that both algorithms produce consensus functions that generally perform slightly better than the best individual scoring function on a given dataset.     

Further development and validation of empirical scoring functions for structure-based binding affinity prediction

New empirical scoring functions have been developed to estimate the binding affinity of a given protein-ligand complex with known three-dimensional structure. These scoring functions include terms accounting for van der Waals interaction, hydrogen bonding, deformation penalty, and hydrophobic effect. A special feature is that three different algorithms have been implemented to calculate the hydrophobic effect term, which results in three parallel scoring functions. All three scoring functions are calibrated through multivariate regression analysis of a set of 200 protein-ligand complexes and they reproduce the binding free energies of the entire training set with standard deviations of 2.2 kcal/mol, 2.1 kcal/mol, and 2.0 kcal/mol, respectively. These three scoring functions are further combined into a consensus scoring function, X-CSCORE. When tested on an independent set of 30 protein-ligand complexes, X-CSCORE is able to predict their binding free energies with a standard deviation of 2.2 kcal/mol. The potential application of X-CSCORE to molecular docking is also investigated. Our results show that this consensus scoring function improves the docking accuracy considerably when compared to the conventional force field computation used for molecular docking.     

An improved PMF scoring function for universally predicting the interactions of a ligand with protein, DNA, and RNA

An improved potential mean force (PMF) scoring function, named KScore, has been developed by using 23 redefined ligand atom types and 17 protein atom types, as well as 28 newly introduced atom types for nucleic acids (DNA and RNA). Metal ions and water molecules embedded in the binding sites of receptors are considered explicitly by two newly defined atom types. The individual potential terms were devised on the basis of the high-resolution crystal and NMR structures of 2,422 protein-ligand complexes, 300 DNA-ligand complexes, and 97 RNA-ligand complexes. The optimized atom pairwise distances and minima of the potentials overcome some of the disadvantages and ambiguities of current PMF potentials; thus, they more reasonably explain the atomic interaction between receptors and ligands. KScore was validated against five test sets of protein-ligand complexes and two sets of nucleic-acid-ligand complexes. The results showed acceptable correlations between KScore scores and experimentally determined binding affinities (log K i's or binding free energies). In particular, KScore can be used to rank the binding of ligands with metalloproteins; the linear correlation coefficient ( R) for the test set is 0.65. In addition to reasonably ranking protein-ligand interactions, KScore also yielded good results for scoring DNA/RNA--ligand interactions; the linear correlation coefficients for DNA-ligand and RNA-ligand complexes are 0.68 and 0.81, respectively. Moreover, KScore can appropriately reproduce the experimental structures of ligand-receptor complexes. Thus, KScore is an appropriate scoring function for universally ranking the interactions of ligands with protein, DNA, and RNA.     

Novel, customizable scoring functions, parameterized using N-PLS, for structure-based drug discovery

The ability to accurately predict biological affinity on the basis of in silico docking to a protein target remains a challenging goal in the CADD arena. Typically, "standard" scoring functions have been employed that use the calculated docking result and a set of empirical parameters to calculate a predicted binding affinity. To improve on this, we are exploring novel strategies for rapidly developing and tuning "customized" scoring functions tailored to a specific need. In the present work, three such customized scoring functions were developed using a set of 129 high-resolution protein-ligand crystal structures with measured Ki values. The functions were parametrized using N-PLS (N-way partial least squares), a multivariate technique well-known in the 3D quantitative structure-activity relationship field. A modest correlation between observed and calculated pKi values using a standard scoring function (r2 = 0.5) could be improved to 0.8 when a customized scoring function was applied. To mimic a more realistic scenario, a second scoring function was developed, not based on crystal structures but exclusively on several binding poses generated with the Flo+ docking program. Finally, a validation study was conducted by generating a third scoring function with 99 randomly selected complexes from the 129 as a training set and predicting pKi values for a test set that comprised the remaining 30 complexes. Training and test set r2 values were 0.77 and 0.78, respectively. These results indicate that, even without direct structural information, predictive customized scoring functions can be developed using N-PLS, and this approach holds significant potential as a general procedure for predicting binding affinity on the basis of in silico docking.     

Assessing the performance of the molecular mechanics/Poisson Boltzmann surface area and molecular mechanics/generalized Born surface area methods. II. The accuracy of ranking poses generated from docking

In molecular docking, it is challenging to develop a scoring function that is accurate to conduct high-throughput screenings. Most scoring functions implemented in popular docking software packages were developed with many approximations for computational efficiency, which sacrifices the accuracy of prediction. With advanced technology and powerful computational hardware nowadays, it is feasible to use rigorous scoring functions, such as molecular mechanics/Poisson Boltzmann surface area (MM/PBSA) and molecular mechanics/generalized Born surface area (MM/GBSA) in molecular docking studies. Here, we systematically investigated the performance of MM/PBSA and MM/GBSA to identify the correct binding conformations and predict the binding free energies for 98 protein-ligand complexes. Comparison studies showed that MM/GBSA (69.4%) outperformed MM/PBSA (45.5%) and many popular scoring functions to identify the correct binding conformations. Moreover, we found that molecular dynamics simulations are necessary for some systems to identify the correct binding conformations. Based on our results, we proposed the guideline for MM/GBSA to predict the binding conformations. We then tested the performance of MM/GBSA and MM/PBSA to reproduce the binding free energies of the 98 protein-ligand complexes. The best prediction of MM/GBSA model with internal dielectric constant 2.0, produced a Spearman's correlation coefficient of 0.66, which is better than MM/PBSA (0.49) and almost all scoring functions used in molecular docking. In summary, MM/GBSA performs well for both binding pose predictions and binding free-energy estimations and is efficient to re-score the top-hit poses produced by other less-accurate scoring functions.     

Evaluation of several two-step scoring functions based on linear interaction energy, effective ligand size, and empirical pair potentials for prediction of protein-ligand binding geometry and free energy

The performances of several two-step scoring approaches for molecular docking were assessed for their ability to predict binding geometries and free energies. Two new scoring functions designed for "step 2 discrimination" were proposed and compared to our CHARMM implementation of the linear interaction energy (LIE) approach using the Generalized-Born with Molecular Volume (GBMV) implicit solvation model. A scoring function S1 was proposed by considering only "interacting" ligand atoms as the "effective size" of the ligand and extended to an empirical regression-based pair potential S2. The S1 and S2 scoring schemes were trained and 5-fold cross-validated on a diverse set of 259 protein-ligand complexes from the Ligand Protein Database (LPDB). The regression-based parameters for S1 and S2 also demonstrated reasonable transferability in the CSARdock 2010 benchmark using a new data set (NRC HiQ) of diverse protein-ligand complexes. The ability of the scoring functions to accurately predict ligand geometry was evaluated by calculating the discriminative power (DP) of the scoring functions to identify native poses. The parameters for the LIE scoring function with the optimal discriminative power (DP) for geometry (step 1 discrimination) were found to be very similar to the best-fit parameters for binding free energy over a large number of protein-ligand complexes (step 2 discrimination). Reasonable performance of the scoring functions in enrichment of active compounds in four different protein target classes established that the parameters for S1 and S2 provided reasonable accuracy and transferability. Additional analysis was performed to definitively separate scoring function performance from molecular weight effects. This analysis included the prediction of ligand binding efficiencies for a subset of the CSARdock NRC HiQ data set where the number of ligand heavy atoms ranged from 17 to 35. This range of ligand heavy atoms is where improved accuracy of predicted ligand efficiencies is most relevant to real-world drug design efforts.