ABSTRACT
Accurate prediction of intermolecular interaction energies is a fundamental challenge in electronic structure theory due to their subtle character and small magnitudes relative to total molecular energies. Symmetry adapted perturbation theory (SAPT) provides rigorous quantum mechanical means for computing such quantities directly and accurately, but for a computational cost of at least O(N5), where N is the number of atoms. Here, we report machine learned models of SAPT components with a computational cost that scales asymptotically linearly, O(N). We use modified multi-target Behler-Parrinello neural networks and specialized intermolecular symmetry functions to address the idiosyncrasies of the intermolecular problem, achieving 1.2 kcal mol-1 mean absolute errors on a test set of hydrogen bound complexes including structural data extracted from the Cambridge Structural Database and Protein Data Bank, spanning an interaction energy range of 20 kcal mol-1. Additionally, we recover accurate predictions of the physically meaningful SAPT component energies, of which dispersion and induction/polarization were the easiest to predict and electrostatics and exchange-repulsion are the most difficult.
ABSTRACT
In May and August, 2016, several pharmaceutical companies convened to discuss and compare experiences with Free Energy Perturbation (FEP). This unusual synchronization of interest was prompted by Schrödinger's FEP+ implementation and offered the opportunity to share fresh studies with FEP and enable broader discussions on the topic. This article summarizes key conclusions of the meetings, including a path forward of actions for this group to aid the accelerated evaluation, application and development of free energy and related quantitative, structure-based design methods.
Subject(s)
Drug Discovery/methods , Pharmaceutical Preparations/chemistry , Drug Design , Drug Industry , Humans , Molecular Structure , Software , Structure-Activity Relationship , ThermodynamicsABSTRACT
A kinome-wide selectivity screen of >20000 compounds with a rich representation of many structural classes has been completed. Analysis of the selectivity patterns for each class shows that a broad spectrum of structural scaffolds can achieve specificity for many kinase families. Kinase selectivity and potency are inversely correlated, a trend that is also found in a large set of kinase functional data. Although selective and nonselective compounds are mostly similar in their physicochemical characteristics, we identify specific features that are present more frequently in compounds that bind to many kinases. Our results support a scaffold-oriented approach for building compound collections to screen kinase targets.
Subject(s)
Phosphotransferases/antagonists & inhibitors , Phosphotransferases/chemistry , Protein Kinase Inhibitors/chemistry , Quantitative Structure-Activity Relationship , Small Molecule Libraries , Cyclin-Dependent Kinase 2/antagonists & inhibitors , Cyclin-Dependent Kinase 2/chemistry , ErbB Receptors/antagonists & inhibitors , ErbB Receptors/chemistry , High-Throughput Screening Assays , Protein Binding , Sequence Homology, Amino Acid , fms-Like Tyrosine Kinase 3/antagonists & inhibitors , fms-Like Tyrosine Kinase 3/chemistryABSTRACT
A series of inhibitors of mammalian 15-lipoxygenase based on tryptamine and homotryptamine scaffolds is described. Compounds with aryl substituents at C-2 of the indole core of tryptamine and homotryptamine sulfonamides (e.g., 37a-p) proved to be potent inhibitors of the isolated enzyme. Selected compounds also demonstrated desirable inhibition selectivities over isozymes 5- and P-12-LO.
