Do Amino-Oxetanes Resemble Amides? A Matched Molecular Pairs Property and Structural Comparison J. Med. Chem. ASAP Oxetanes have long been used in medicinal chemistry campaigns, yet have remained a controversial choice in design due to the lack of this chemotype amongst marketed drugs, until now. This controversy has largely been settled, thanks to the

ActivityFinder: Toward the Fully Automatic Integration of Structural and Binding Affinity Data As the drug discovery industry moves towards using AI models to predict protein-ligand complexes and compound activities, there has never been a greater need for robust and automated curation of structural datasets. Public databases, such as the Protein Data Bank (https://www.rcsb.org/) for protein

Design, modular synthesis and screening of 58 shape-diverse 3-D fragments     Fragment screening is a tried and tested method of finding quality hit matter for the target of interest, fragment-based drug discovery (FBDD) is thus widely adopted in both academic and industrial settings where ligand-target co-crystal or Cryo-EM structures can be obtained. For

Practically Significant Method Comparison Protocols for Machine Learning in Small Molecule Drug Discovery This open access hands-on perspective offers best-practice recommendations for rigorous method comparison in the domain of ML-based small-molecule property prediction. Given the burgeoning nature of the subject matter, the authors fulfil a contemporary community need in provisioning, by example, a state-of-the-art

Discovery of the Clinical Candidate S‑892216: A Second-Generation of SARS-CoV‑2 3CL Protease Inhibitor for Treating COVID-19 Coronavirus infections are widespread, with symptoms of varying severity depending on the virus and the individual circumstances of the patient. In 2022, Shionogi disclosed the structure of ensitrelvir, an orally bioavailable non-covalent, non-peptidomimetic Coronavirus Main Protease (CoV MPro) inhibitor

AI in drug design: evolution or revolution? Darren sets out to explore the impact of artificial intelligence (AI) on the pharmaceutical industry, particularly in the drug discovery sector. He begins by setting out the different AI methods, such as machine learning (ML; models that learn from previous data), deep learning (ML models that mimic human

Efficient Crystallization of Apo Sirt2 for Small-Molecule Soaking and Structural Analysis of Ligand Interactions   J. Med. Chem. 2024, 67, 15, 12461–12462 Sirtuin proteins (Silent Information Regulators, Sirt1-7) are NAD+-dependent enzymes that have a range of important functions as metabolic regulators, depending on their subcellular localisation. This family of enzymes is relevant to the discovery

Exploration of structure-activity relationships for the SARS-CoV-2 macrodomain from shape-based fragment linking and active learning One of the known challenges in drug discovery is finding good initial hit compounds in the vast expanse of druglike chemical space. Traditional high throughput screening (HTS) libraries only cover a small area of chemical space and are costly to

Cheminformatic Analysis of Core-Atom Transformations in Pharmaceutically Relevant Heteroaromatics J. Med. Chem., 2025, 68, 6, 6027–6040 The art of medicinal chemistry involves discovering molecules that represent a compromise between target potency and in-vivo pharmacokinetic properties. Only after a successful balancing act between these two (sometimes conflicting) factors has been achieved, is it possible to propose a

Visualising lead optimisation series using reduced graphs Journal of Cheminformatics This month’s paper of the month has been recently published by our Senior Computational Chemist, Jess Stacey, for her PhD work that she undertook at the University of Sheffield. They were using Reduced Graphs (RG) to visualise lead optimisation series, indicating areas that have been