Abstract
Hit-to-lead virtual screening frequently relies on a cascade of computational methods that starts with rapid calculations applied to a large number of compounds and ends with more expensive computations restricted to a subset of compounds that passed initial filters. This work focuses on set up protocols for alchemical free energy (AFE) scoring in the context of a Docking–MM/PBSA–AFE cascade. A dataset of 15 congeneric inhibitors of the ACK1 protein was used to evaluate the performance of AFE set up protocols that varied in the steps taken to prepare input files (using previously docked and best scored poses, manual selection of poses, manual placement of binding site water molecules). The main finding is that use of knowledge derived from X-ray structures to model binding modes, together with the manual placement of a bridging water molecule, improves the R2 from 0.45 ± 0.06 to 0.76 ± 0.02 and decreases the mean unsigned error from 2.11 ± 0.08 to 1.24 ± 0.04 kcal mol-1. By contrast a brute force automated protocol that increased the sampling time ten-fold lead to little improvements in accuracy. Besides, it is shown that for the present dataset hysteresis can be used to flag poses that need further attention even without prior knowledge of experimental binding affinities.
Highlights
There is continuous interest in computational methods to decrease time and costs of hit-tolead and lead optimization efforts in preclinical drug discovery [1]
The present study investigates the suitability of alchemical free energy (AFE) methods for improving on this multistep docking and scoring protocol by means of a further re-scoring of ligands
This work has explored the viability of using alchemical free energy methods as a final filter in a cascade of computational methods for hit-to-lead virtual screens in the context of a dataset of ACK1 inhibitors
Summary
There is continuous interest in computational methods to decrease time and costs of hit-tolead and lead optimization efforts in preclinical drug discovery [1]. A recurring topic in computational chemistry is the use of virtual in silico screens to find ligands for proteins [2, 3]. The goal is to filter via a cascade of computational methods a large library to focus experimental efforts on a small number of molecules. Inexpensive methodologies are applied first to eliminate a large number of poorly suited molecules, with more expensive calculations reserved to a subset of promising ligands.
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