Design of a Lead-Like Cysteine-Targeting Covalent Library and the Identification of Hits to Cys55 of Bfl-1.

Covalent hit identification is a viable approach to identify chemical starting points against difficult-to-drug targets. While most researchers screen libraries of <2k electrophilic fragments, focusing on lead-like compounds can be advantageous in terms of finding hits with improved affinity and with a better chance of identifying cryptic pockets. However, due to the increased molecular complexity, larger numbers of compounds (>10k) are desirable to ensure adequate coverage of chemical space.

Exploring the ability of machine learning-based virtual screening models to identify the functional groups responsible for binding.

Many recently proposed structure-based virtual screening models appear to be able to accurately distinguish high affinity binders from non-binders. However, several recent studies have shown that they often do so by exploiting ligand-specific biases in the dataset, rather than identifying favourable intermolecular interactions in the input protein-ligand complex. In this work we propose a novel approach for assessing the extent to which machine learning-based virtual screening models are able to identify the functional groups responsible for binding.

A Small Step Toward Generalizability: Training a Machine Learning Scoring Function for Structure-Based Virtual Screening.

Over the past few years, many machine learning-based scoring functions for predicting the binding of small molecules to proteins have been developed. Their objective is to approximate the distribution which takes two molecules as input and outputs the energy of their interaction. Only a scoring function that accounts for the interatomic interactions involved in binding can accurately predict binding affinity on unseen molecules.

Incorporating Target-Specific Pharmacophoric Information into Deep Generative Models for Fragment Elaboration.

Despite recent interest in deep generative models for scaffold elaboration, their applicability to fragment-to-lead campaigns has so far been limited. This is primarily due to their inability to account for local protein structure or a user's design hypothesis. We propose a novel method for fragment elaboration, STRIFE, that overcomes these issues.

AI in 3D compound design.

The success of Artificial Intelligence (AI) across a wide range of domains has fuelled significant interest in its application to designing novel compounds and screening compounds against a specific target. However, many existing AI methods either do not account for the 3D structure of the target at all or struggle to capture meaningful spatial information from the target. In this Opinion, we highlight a range of recent structure-aware approaches which utilise deep learning for compound design and virtual screening.

Deep generative design with 3D pharmacophoric constraints.

Generative models have increasingly been proposed as a solution to the molecular design problem. However, it has proved challenging to control the design process or incorporate prior knowledge, limiting their practical use in drug discovery. In particular, generative methods have made limited use of three-dimensional (3D) structural information even though this is critical to binding.