A sobering assessment of small-molecule force field methods for low energy conformer predictions

We have carried out a large scale computational investigation to assess the utility of common small-molecule force fields for computational screening of low energy conformers of typical organic molecules. Using statistical analyses on the energies and relative rankings of up to 250 diverse conformers of 700 different molecular structures, we find that energies from widely used classical force fields (MMFF94, UFF, and GAFF) show unconditionally poor energy and rank correlation with semiempirical (PM7) and Kohn–Sham density functional theory (DFT) energies calculated at PM7 and DFT optimized geometries. In contrast, semiempirical PM7 calculations show significantly better correlation with DFT calculations and generally better geometries. With these results, we make recommendations to more reliably carry out conformer screening

Atomic radius and charge parameter uncertainty in biomolecular solvation energy calculations

Atomic radii and charges are two major parameters used in implicit solvent electrostatics and energy calculations. The optimization problem for charges and radii is under-determined, leading to uncertainty in the values of these parameters and in the results of solvation energy calculations using these parameters. This paper presents a new method for quantifying this uncertainty in implicit solvation calculations of small molecules using surrogate models based on generalized polynomial chaos (gPC) expansions. There are relatively few atom types used to specify radii parameters in implicit solvation calculations; therefore, surrogate models for these low-dimensional spaces could be constructed using least-squares fitting. However, there are many more types of atomic charges; therefore, construction of surrogate models for the charge parameter space requires compressed sensing combined with an iterative rotation method to enhance problem sparsity. We demonstrate the application of the method by presenting results for the uncertainties in small molecule solvation energies based on these approaches. The method presented in this paper is a promising approach for efficiently quantifying uncertainty in a wide range of force field parameterization problems, including those beyond continuum solvation calculations.The intent of this study is to provide a way for developers of implicit solvent model parameter sets to understand the sensitivity of their target properties (solvation energy) on underlying choices for solute radius and charge parameters

Complex macrocycle exploration: parallel, heuristic, and constraint-based conformer generation using ForceGen.

ForceGen is a template-free, non-stochastic approach for 2D to 3D structure generation and conformational elaboration for small molecules, including both non-macrocycles and macrocycles. For conformational search of non-macrocycles, ForceGen is both faster and more accurate than the best of all tested methods on a very large, independently curated benchmark of 2859 PDB ligands. In this study, the primary results are on macrocycles, including results for 431 unique examples from four separate benchmarks. These include complex peptide and peptide-like cases that can form networks of internal hydrogen bonds. By making use of new physical movements ("flips" of near-linear sub-cycles and explicit formation of hydrogen bonds), ForceGen exhibited statistically significantly better performance for overall RMS deviation from experimental coordinates than all other approaches. The algorithmic approach offers natural parallelization across multiple computing-cores. On a modest multi-core workstation, for all but the most complex macrocycles, median wall-clock times were generally under a minute in fast search mode and under 2 min using thorough search. On the most complex cases (roughly cyclic decapeptides and larger) explicit exploration of likely hydrogen bonding networks yielded marked improvements, but with calculation times increasing to several minutes and in some cases to roughly an hour for fast search. In complex cases, utilization of NMR data to constrain conformational search produces accurate conformational ensembles representative of solution state macrocycle behavior. On macrocycles of typical complexity (up to 21 rotatable macrocyclic and exocyclic bonds), design-focused macrocycle optimization can be practically supported by computational chemistry at interactive time-scales, with conformational ensemble accuracy equaling what is seen with non-macrocyclic ligands. For more complex macrocycles, inclusion of sparse biophysical data is a helpful adjunct to computation

QM7-X: A comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules

We introduce QM7-X, a comprehensive dataset of 42 physicochemical properties for $\approx$ 4.2 M equilibrium and non-equilibrium structures of small organic molecules with up to seven non-hydrogen (C, N, O, S, Cl) atoms. To span this fundamentally important region of chemical compound space (CCS), QM7-X includes an exhaustive sampling of (meta-)stable equilibrium structures - comprised of constitutional/structural isomers and stereoisomers, e.g., enantiomers and diastereomers (including cis-/trans- and conformational isomers) - as well as 100 non-equilibrium structural variations thereof to reach a total of $\approx$ 4.2 M molecular structures. Computed at the tightly converged quantum-mechanical PBE0+MBD level of theory, QM7-X contains global (molecular) and local (atom-in-a-molecule) properties ranging from ground state quantities (such as atomization energies and dipole moments) to response quantities (such as polarizability tensors and dispersion coefficients). By providing a systematic, extensive, and tightly-converged dataset of quantum-mechanically computed physicochemical properties, we expect that QM7-X will play a critical role in the development of next-generation machine-learning based models for exploring greater swaths of CCS and performing in silico design of molecules with targeted properties

Reaction between Peroxy and Alkoxy Radicals Can Form Stable Adducts

Peroxy (RO2) and alkoxy (RO) radicals are prototypical intermediates in any hydrocarbon oxidation. In this work, we use computational methods to (1) study the mechanism and kinetics of the RO2 + OH reaction for previously unexplored “R” structures (R = CH(O)CH2 and R = CH3C(O)) and (2) investigate a hitherto unaccounted channel of molecular growth, R′O2 + RO. On the singlet surface, these reactions rapidly form ROOOH and R′OOOR adducts, respectively. The former decomposes to RO + HO2 and R(O)OH + O2 products, while the main decomposition channel for the latter is back to the reactant radicals. Decomposition rates of R′OOOR adducts varied between 103 and 0.015 s–1 at 298 K and 1 atm. The most long-lived R′OOOR adducts likely account for some fraction of the elemental compositions detected in the atmosphere that are commonly assigned to stable covalently bound dimers.Peer reviewe

Ligand-based drug design : I. conformational studies of GBR 12909 analogs as cocaine antagonists; II. 3d-QSAR studies of salvinorin a analogs as kappa opioid agonists

Ligand-based drug design (LBDD) techniques are applied when the structure of the receptor is unknown but when a series of compounds or ligands have been identified that show the biological activity of the interest. Generally, availability of a series of compounds with high activity, with no activity, and also with a range of intermediate activities for the desired biological target is required. It is common that structures of membrane-bound proteins (for example, monoamine transporter proteins and opioid receptor proteins) are unknown as these proteins are notoriously difficult to crystallize. In Part I of this study, analogs of the flexible dopamine reuptake inhibitor, GBR 12909, may have potential usefulness in the treatment of cocaine abuse. As a first step in the 3D-QSAR modeling of the dopamine transporter (DAT)/serotonin transporter (SERT) selectivity of these compounds, conformational analysis of a piperazine and related piperidine analog of GBR12909 is performed. These analogs have eight rotatable bonds and are somewhat easier to deal with computationally than the parent compound. Ensembles of conformers consisting of local minima on the potential energy surface of the molecule were generated in the vacuum phase and implicit solvent (also known as continuum solvent) by random search conformational analysis using the molecular mechanics methods and the Tripos and MMFF94 force fields. These conformer populations were classified by relative energy, molecular shape, and their behavior in 2D torsional angle space in order to evaluate their sensitivity to the choice of charges and force field. Some differences were noted in the conformer populations due to differences in the treatment of the tertiary amine nitrogen and ether oxygen atom types by the force fields. In Part II of this study, 3D-QSAR studies of salvinorin A analogs as kappa opioid (K) receptor agonists were performed. Salvinorin A is a naturally-occurring diterpene from the plant Salvia divinorum which activates the kappa opioid receptor (KOR) selectively and potently. It is the only known natural non-nitrogenous agent active at the human KOR. Salvinorin A may represent a novel lead compound with possible potential in the treatment of addiction and pain. The primary aim of the current study was to develop Comparative Molecular Field Analysis (CoMFA) models to clarify the correlation between the molecular features of the 2-position analogs of salvinorin A and their KOR binding affinity. The final, stable CoMFA model has predictivity given by q2 of 0.62 and fit given by r2 of 0.86. The steric and electrostatic contributions were 47% and 53%, respectively. The CoMFA contour map indicated that the presence of a negative environment and steric region near the 2-position would lead to improved binding affinity at the KOR. Novel salvinorin A analogs with improved binding affinity were predicted based on the stable and predictive CoMFA model. Novel analogs were synthesized by Dr. Thomas Prisinzano of the University of Iowa and preliminary biological results are available from the Rothman laboratory at the National Institute on Drug Abuse. These novel analogs appear to be KOR selective