The parametrization and validation of the OPLS3 force field for small molecules and proteins are reported. Enhancements with respect to the previous version (OPLS2.1) include the addition of off-atom charge sites to represent halogen bonding and aryl nitrogen lone pairs as well as a complete refit of peptide dihedral parameters to better model the native structure of proteins. To adequately cover medicinal chemical space, OPLS3 employs over an order of magnitude more reference data and associated parameter types relative to other commonly used small molecule force fields (e.g., MMFF and OPLS_2005). As a consequence, OPLS3 achieves a high level of accuracy across performance benchmarks that assess small molecule conformational propensities and solvation. The newly fitted peptide dihedrals lead to significant improvements in the representation of secondary structure elements in simulated peptides and native structure stability over a number of proteins. Together, the improvements made to both the small molecule and protein force field lead to a high level of accuracy in predicting protein-ligand binding measured over a wide range of targets and ligands (less than 1 kcal/mol RMS error) representing a 30% improvement over earlier variants of the OPLS force field.

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 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 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.3

We provide an overview of the IMPACT molecular mechanics program with an emphasis on recent developments and a description of its current functionality. With respect to core molecular mechanics technologies we include a status report for the fixed charge and polarizable force fields that can be used with the program and illustrate how the force fields, when used together with new atom typing and parameter assignment modules, have greatly expanded the coverage of organic compounds and medicinally relevant ligands. As we discuss in this review, explicit solvent simulations have been used to guide our design of implicit solvent models based on the generalized Born framework and a novel nonpolar estimator that have recently been incorporated into the program. With IMPACT it is possible to use several different advanced conformational sampling algorithms based on combining features of molecular dynamics and Monte Carlo simulations. The program includes two specialized molecular mechanics modules: Glide, a high-throughput docking program, and QSite, a mixed quantum mechanics/molecular mechanics module. These modules employ the IMPACT infrastructure as a starting point for the construction of the protein model and assignment of molecular mechanics parameters, but have then been developed to meet specialized objectives with respect to sampling and the energy function.

The accurate prediction of protein-ligand binding free energies is a primary objective in computer-aided drug design. The solvation free energy of a small molecule provides a surrogate to the desolvation of the ligand in the thermodynamic process of protein-ligand binding. Here, we use explicit solvent molecular dynamics free energy perturbation to predict the absolute solvation free energies of a set of 239 small molecules, spanning diverse chemical functional groups commonly found in drugs and drug-like molecules. We also compare the performance of absolute solvation free energies obtained using the OPLS_2005 force field with two other commonly used small molecule force fields-general AMBER force field (GAFF) with AM1-BCC charges and CHARMm-MSI with CHelpG charges. Using the OPLS_2005 force field, we obtain high correlation with experimental solvation free energies (R(2) = 0.94) and low average unsigned errors for a majority of the functional groups compared to AM1-BCC/GAFF or CHelpG/CHARMm-MSI. However, OPLS_2005 has errors of over 1.3 kcal/mol for certain classes of polar compounds. We show that predictions on these compound classes can be improved by using a semiempirical charge assignment method with an implicit bond charge correction.

Building upon the OPLS3 force field we report on an enhanced model, OPLS3e, that further extends its coverage of medicinally relevant chemical space by addressing limitations in chemotype transferability. OPLS3e accomplishes this by incorporating new parameter types that recognize moieties with greater chemical specificity and integrating an on-the-fly parametrization approach to the assignment of partial charges. As a consequence, OPLS3e leads to greater accuracy against performance benchmarks that assess small molecule conformational propensities, solvation, and protein–ligand binding.

We report on the development and validation of the OPLS4 force field. OPLS4 builds upon our previous work with OPLS3e to improve model accuracy on challenging regimes of drug-like chemical space that includes molecular ions and sulfurcontaining moieties. A novel parametrization strategy for charged species, which can be extended to other systems, is introduced. OPLS4 leads to improved accuracy on benchmarks that assess small-molecule solvation and protein−ligand binding.

The OPLS all‐atom (AA) force field for organic and biomolecular systems has been expanded to include carbohydrates. Starting with reported nonbonded parameters of alcohols, ethers, and diols, torsional parameters were fit to reproduce results from ab initio calculations on the hexopyranoses, α,β‐d‐glucopyranose, α,β‐d‐mannopyranose, α,β‐d‐galactopyranose, methyl α,β‐d‐glucopyranoside, and methyl α,β‐d‐mannopyranoside. In all, geometry optimizations were carried out for 144 conformers at the restricted Hartree–Fock (RHF)/6–31G* level. For the conformers with a relative energy within 3 kcal/mol of the global minima, the effects of electron correlation and basis‐set extension were considered by performing single‐point calculations with density functional theory at the B3LYP/6–311+G** level. The torsional parameters for the OPLS‐AA force field were parameterized to reproduce the energies and structures of these 44 conformers. The resultant force field reproduces the ab initio calculated energies with an average unsigned error of 0.41 kcal/mol. The α/β ratios as well as the relative energies between the isomeric hexopyranoses are in good accord with the ab initio results. The predictive abilities of the force field were also tested against RHF/6–31G* results for d‐allopyranose with excellent success; a surprising discovery is that the lowest energy conformer of d‐allopyranose is a β anomer. © 1997 John Wiley & Sons, Inc. J Comput Chem 18: 1955–1970, 1997

Standard force fields used in biomolecular computing describe electrostatic interactions in terms of fixed, usually atom-centered, charges. Real physical systems, however, polarize substantially when placed in a high-dielectric medium such as water--or even when a strongly charged system approaches a neutral body in the gas phase. Such polarization strongly affects the geometry and energetics of molecular recognition. First introduced more than 20 years ago, polarizable force fields seek to account for appropriate variations in charge distribution with dielectric environment. Over the past five years, an accelerated pace of development of such force fields has taken place on systems ranging from liquid water to metalloenzymes. Noteworthy progress has been made in better understanding the capabilities and limitations of polarizable models for water and in the formulation and utilization of complete specifically parameterized polarizable force fields for peptides and proteins.