Approaches for machine learning intermolecular interaction energies and application to energy components from symmetry adapted perturbation theory.

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(N), 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).

Collaborating to improve the use of free-energy and other quantitative methods in drug discovery.

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.

To measure is to know: an approach to CADD performance metrics.

With the financial and productivity challenges currently facing the pharmaceutical industry, there is constant pressure to justify resources and improve efficiency. With process-driven activities, understanding the contribution of these resources is reasonably straightforward. By contrast, measuring the contribution of knowledge workers is less obvious.

Trends in kinase selectivity: insights for target class-focused library screening.

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.

A simple strategy for mitigating the effect of data variability on the identification of active chemotypes from high-throughput screening data.

Among the several goals of a high-throughput screening campaign is the identification of as many active chemotypes as possible for further evaluation. Often, however, the number of concentration response curves (e.g.