This presentation will describe how structural biology, molecular pharmacology, and medicinal chemistry studies can be combined with molecular modeling and chemoinformatics analyses for a more accurate description and prediction of structural determinants of protein-ligand binding, functional activity, and selectivity.The challenges and possibilities of structural chemogenomics studies will be discussed, including the integration of large volumes of heterogeneous pharmacological and chemical data for different protein targets and the development of structure-based virtual screening and computer-aided drug design approaches to discover novel small molecule ligands with well defined functional activity and protein selectivity profiles.The potential of molecular dynamics simulation methods to complement hybrid structural biology studies will be demonstrated for the investigation the mechanisms of conformational selection and protein-ligand binding kinetics.In the final part of the presentation structural protein-ligand interaction databases will be described that link structure-based protein-ligand interaction maps to protein ligand topology and can be used as structural chemogenomics tools to navigate medicinal chemistry space.

Designing tight-binding ligands is a primary objective of small-molecule drug discovery. Over the past few decades, free-energy calculations have benefited from improved force fields and sampling algorithms, as well as the advent of low-cost parallel computing. However, it has proven to be challenging to reliably achieve the level of accuracy that would be needed to guide lead optimization (∼5× in binding affinity) for a wide range of ligands and protein targets. Not surprisingly, widespread commercial application of free-energy simulations has been limited due to the lack of large-scale validation coupled with the technical challenges traditionally associated with running these types of calculations. Here, we report an approach that achieves an unprecedented level of accuracy across a broad range of target classes and ligands, with retrospective results encompassing 200 ligands and a wide variety of chemical perturbations, many of which involve significant changes in ligand chemical structures. In addition, we have applied the method in prospective drug discovery projects and found a significant improvement in the quality of the compounds synthesized that have been predicted to be potent. Compounds predicted to be potent by this approach have a substantial reduction in false positives relative to compounds synthesized on the basis of other computational or medicinal chemistry approaches. Furthermore, the results are consistent with those obtained from our retrospective studies, demonstrating the robustness and broad range of applicability of this approach, which can be used to drive decisions in lead optimization.

Predicting protein-ligand binding free energies is a central aim of computational structure-based drug design (SBDD)--improved accuracy in binding free energy predictions could significantly reduce costs and accelerate project timelines in lead discovery and optimization. The recent development and validation of advanced free energy calculation methods represents a major step toward this goal. Accurately predicting the relative binding free energy changes of modifications to ligands is especially valuable in the field of fragment-based drug design, since fragment screens tend to deliver initial hits of low binding affinity that require multiple rounds of synthesis to gain the requisite potency for a project. In this study, we show that a free energy perturbation protocol, FEP+, which was previously validated on drug-like lead compounds, is suitable for the calculation of relative binding strengths of fragment-sized compounds as well. We study several pharmaceutically relevant targets with a total of more than 90 fragments and find that the FEP+ methodology, which uses explicit solvent molecular dynamics and physics-based scoring with no parameters adjusted, can accurately predict relative fragment binding affinities. The calculations afford R(2)-values on average greater than 0.5 compared to experimental data and RMS errors of ca. 1.1 kcal/mol overall, demonstrating significant improvements over the docking and MM-GBSA methods tested in this work and indicating that FEP+ has the requisite predictive power to impact fragment-based affinity optimization projects.

Recent advances in improved force fields and sampling methods have made it possible for the accurate calculation of protein–ligand binding free energies. Alchemical free energy perturbation (FEP) using an explicit solvent model is one of the most rigorous methods to calculate relative binding free energies. However, for cases where there are high energy barriers separating the relevant conformations that are important for ligand binding, the calculated free energy may depend on the initial conformation used in the simulation due to the lack of complete sampling of all the important regions in phase space. This is particularly true for ligands with multiple possible binding modes separated by high energy barriers, making it difficult to sample all relevant binding modes even with modern enhanced sampling methods. In this paper, we apply a previously developed method that provides a corrected binding free energy for ligands with multiple binding modes by combining the free energy results from multiple alchemical FEP calculations starting from all enumerated poses, and the results are compared with Glide docking and MM-GBSA calculations. From these calculations, the dominant ligand binding mode can also be predicted. We apply this method to a series of ligands that bind to c-Jun N-terminal kinase-1 (JNK1) and obtain improved free energy results. The dominant ligand binding modes predicted by this method agree with the available crystallography, while both Glide docking and MM-GBSA calculations incorrectly predict the binding modes for some ligands. The method also helps separate the force field error from the ligand sampling error, such that deviations in the predicted binding free energy from the experimental values likely indicate possible inaccuracies in the force field. An error in the force field for a subset of the ligands studied was identified using this method, and improved free energy results were obtained by correcting the partial charges assigned to the ligands. This improved the root-mean-square error (RMSE) for the predicted binding free energy from 1.9 kcal/mol with the original partial charges to 1.3 kcal/mol with the corrected partial charges.

Designing tight binding ligands is a primary objective of small molecule drug discovery. Over the past few decades, free energy calculations have benefited from improved force fields and sampling algorithms, as well as the advent of low cost parallel computing. However, it has proven to be challenging to reliably achieve the level of accuracy that would be needed to guide lead optimization (~5X in binding affinity) for a wide range of ligands and protein targets. Not surprisingly, widespread commercial application of free energy simulations has been limited due to the lack of large-scale validation coupled with the technical challenges traditionally associated with running these types of calculations. Here, we report an approach that achieves an unprecedented level of accuracy across a broad range of target classes, with retrospective results encompassing 200 ligands and a wide variety of chemical perturbations, many of which involve significant changes in ligand chemical structures. In addition, we have applied the method in prospective drug discovery projects and found a significant improvement in the quality of the compounds synthesized that have been predicted to be potent. Compounds predicted to be potent by this approach have a substantial reduction in false positives relative to compounds synthesized based on other computational or medicinal chemistry approaches. Furthermore, the results are consistent with those obtained from our retrospective studies, demonstrating the robustness and broad range of applicability of this approach, which can be used to drive decisions in lead optimization.

Alchemical free energy calculations hold increasing promise as an aid to drug discovery efforts. However, applications of these techniques in discovery projects have been relatively few, partly because of the difficulty of planning and setting up calculations. Here, we introduce lead optimization mapper, LOMAP, an automated algorithm to plan efficient relative free energy calculations between potential ligands within a substantial library of perhaps hundreds of compounds. In this approach, ligands are first grouped by structural similarity primarily based on the size of a (loosely defined) maximal common substructure, and then calculations are planned within and between sets of structurally related compounds. An emphasis is placed on ensuring that relative free energies can be obtained between any pair of compounds without combining the results of too many different relative free energy calculations (to avoid accumulation of error) and by providing some redundancy to allow for the possibility of error and consistency checking and provide some insight into when results can be expected to be unreliable. The algorithm is discussed in detail and a Python implementation, based on both Schrödinger’s and OpenEye’s APIs, has been made available freely under the BSD license.

Accurate and reliable calculation of protein-ligand binding affinities remains a hotbed of computer-aided drug design research. Despite the potentially large impact FEP (free energy perturbation) may have in drug design projects, practical applications of FEP in industrial contexts have been limited. In this work, we use a recently developed method, FEP/REST (free energy perturbation/replica exchange with solute tempering), to calculate the relative binding affinities for a set of congeneric ligands binding to the CDK2 receptor. We compare the FEP/REST results with traditional FEP/MD (molecular dynamics) results and MM/GBSA (molecular mechanics/Generalized Born Surface Area model) results and examine why FEP/REST performed notably better than these other methods, as well as why certain ligand mutations lead to large increases of the binding affinity while others do not. We also introduce a mathematical framework for assessing the consistency and reliability of the calculations using cycle closures in FEP mutation paths.

We present an algorithm for carrying out Langevin dynamics simulations on complex rigid bodies by incorporating the hydrodynamic resistance tensors for arbitrary shapes into an advanced rotational integration scheme. The integrator gives quantitative agreement with both analytic and approximate hydrodynamic theories for a number of model rigid bodies and works well at reproducing the solute dynamical properties (diffusion constants and orientational relaxation times) obtained from explicitly solvated simulations.

OOPSE is a new molecular dynamics simulation program that is capable of efficiently integrating equations of motion for atom types with orientational degrees of freedom (e.g. "sticky" atoms and point dipoles). Transition metals can also be simulated using the embedded atom method (EAM) potential included in the code. Parallel simulations are carried out using the force-based decomposition method. Simulations are specified using a very simple C-based meta-data language. A number of advanced integrators are included, and the basic integrator for orientational dynamics provides substantial improvements over older quaternion-based schemes.

Quantitative structure–property relationships (QSPR) were developed using a genetic algorithm (GA)-based variable-selection approach with quantum chemical descriptors derived from AM1-based calculations (MOPAC7.0). With the QSPR models, the aqueous solubility of 71 aromatic sulfur-containing carboxylates, including phenylthio, and phenylsulfonyl carboxylates were efficiently estimated and predicted. Using GA-based multivariate linear regression (MLR) with cross-validation procedure, the most important descriptors were selected from a pool of 28 quantum chemical semi-empirical descriptors, including steric and electronic types, to build QSPR models. The molecular descriptors included molecular surface (SA), charges on carboxyl group (QOC), the magnitude of the difference between EHOMO of the solute and ELUMO of water, divided by 100 (EB), which were main factors affecting the aqueous solubility of the compounds of interest. The resulted coefficients R and R2 of 0.9571 and 0.9161 and the prediction residual error sum of squares (PRESS) of 13.1768, revealed that it was accurate and reliable for the model to predict the aqueous solubility of the investigated organic compounds. If two outliers were omitted from the dataset, the resulted coefficients R=0.9619, R2=0.9253, and PRESS=10.3875 were significantly improved. Compared with stepwise regression analysis, the results obtained in this work were better and more reasonable. The best QSPR model were obtained by GA-based MLR. Reasonable mechanisms for aqueous solubility of the sulfur-containing carboxylates were investigated and interpreted.