Towards the prediction of drug solubility in binary solvent mixtures at various temperatures using machine learning.

Drug solubility is an important parameter in the drug development process, yet it is often tedious and challenging to measure, especially for expensive drugs or those available in small quantities. To alleviate these challenges, machine learning (ML) has been applied to predict drug solubility as an alternative approach. However, the majority of existing ML research has focused on the predictions of aqueous solubility and/or solubility at specific temperatures, which restricts the model applicability in pharmaceutical development.

Machine Learning Guided AQFEP: A Fast and Efficient Absolute Free Energy Perturbation Solution for Virtual Screening.

Structure-based methods in drug discovery have become an integral part of the modern drug discovery process. The power of virtual screening lies in its ability to rapidly and cost-effectively explore enormous chemical spaces to select promising ligands for further experimental investigation. Relative free energy perturbation (RFEP) and similar methods are the gold standard for binding affinity prediction in drug discovery hit-to-lead and lead optimization phases, but have high computational cost and the requirement of a structural analog with a known activity.

In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back.

Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving the Schrödinger equations and the increasing computational cost with the size of the molecular system. In response, there has been a surge of interest in leveraging artificial intelligence (AI) and machine learning (ML) techniques to in silico experiments.

Small molecule binding to surface-supported single-site transition-metal reaction centres.

Despite dominating industrial processes, heterogeneous catalysts remain challenging to characterize and control. This is largely attributable to the diversity of potentially active sites at the catalyst-reactant interface and the complex behaviour that can arise from interactions between active sites. Surface-supported, single-site molecular catalysts aim to bring together benefits of both heterogeneous and homogeneous catalysts, offering easy separability while exploiting molecular design of reactivity, though the presence of a surface is likely to influence reaction mechanisms.