Protein-Ligand Scoring with Convolutional Neural Networks

Computational approaches to drug discovery can reduce the time and cost associated with experimental assays and enable the screening of novel chemotypes. Structure-based drug design methods rely on scoring functions to rank and predict binding affinities and poses. The ever-expanding amount of protein-ligand binding and structural data enables the use of deep machine learning techniques for protein-ligand scoring. We describe convolutional neural network (CNN) scoring functions that take as input a comprehensive 3D representation of a protein-ligand interaction. A CNN scoring function automatically learns the key features of protein-ligand interactions that correlate with binding. We train and optimize our CNN scoring functions to discriminate between correct and incorrect binding poses and known binders and non-binders. We find that our CNN scoring function outperforms the AutoDock Vina scoring function when ranking poses both for pose prediction and virtual screening

Small-molecule inhibitor starting points learned from protein–protein interaction inhibitor structure

Motivation: Protein–protein interactions (PPIs) are a promising, but challenging target for pharmaceutical intervention. One approach for addressing these difficult targets is the rational design of small-molecule inhibitors that mimic the chemical and physical properties of small clusters of key residues at the protein–protein interface. The identification of appropriate clusters of interface residues provides starting points for inhibitor design and supports an overall assessment of the susceptibility of PPIs to small-molecule inhibition

Virtual Screening with Gnina 1.0

Virtual screening—predicting which compounds within a specified compound library bind to a target molecule, typically a protein—is a fundamental task in the field of drug discovery. Doing virtual screening well provides tangible practical benefits, including reduced drug development costs, faster time to therapeutic viability, and fewer unforeseen side effects. As with most applied computational tasks, the algorithms currently used to perform virtual screening feature inherent tradeoffs between speed and accuracy. Furthermore, even theoretically rigorous, computationally intensive methods may fail to account for important effects relevant to whether a given compound will ultimately be usable as a drug. Here we investigate the virtual screening performance of the recently released Gnina molecular docking software, which uses deep convolutional networks to score protein-ligand structures. We find, on average, that Gnina outperforms conventional empirical scoring. The default scoring in Gnina outperforms the empirical AutoDock Vina scoring function on 89 of the 117 targets of the DUD-E and LIT-PCBA virtual screening benchmarks with a median 1% early enrichment factor that is more than twice that of Vina. However, we also find that issues of bias linger in these sets, even when not used directly to train models, and this bias obfuscates to what extent machine learning models are achieving their performance through a sophisticated interpretation of molecular interactions versus fitting to non-informative simplistic property distributions

Register Allocation Deconstructed

Register allocation is a fundamental part of any optimizing compiler. Effectively managing the limited register resources of the constrained architectures commonly found in embedded systems is essential in order to maximize code quality. In this paper we deconstruct the register allocation problem into distinct components: coalescing, spilling, move insertion, and assignment. Using an optimal register allocation framework, we empirically evaluate the importance of each of the components, the impact of component integration, and the effectiveness of existing heuristics. We evaluate code quality both in terms of code performance and code size and consider four distinct instruction set architectures: ARM, Thumb, x86, and x86-64. The results of our investigation reveal general principles for register allocation design

A Global Progressive Register Allocator

This paper describes a global progressive register allocator, a register allocator that uses an expressive model of the register allocation problem to quickly find a good allocation and then progressively find better allocations until a provably optimal solution is found or a preset time limit is reached. The key contributions of this paper are an expressive model of global register allocation based on multicommodity network flows that explicitly represents spill code optimization, register preferences, copy insertion, and constant rematerialization; two fast, but effective, heuristic allocators based on this model; and a more elaborate progressive allocator that uses Lagrangian relaxation to compute the optimality of its allocations. Our progressive allocator demonstrates code size improvements as large as 16.75 % compared to a traditional graph allocator. On average, we observe an initial improvement of 3.47%, which increases progressively to 6.84 % as more time is permitted for compilation