Simulation-driven design of stabilized SARS-CoV-2 spike S2 immunogens During the European Federation for Medicinal Chemistry – International Symposium on Medicinal Chemistry (EFMC-ISMC) 2024, I attended an inspiring lecture by Dr Rommie Amaro from University of California in which she enthusiastically demonstrated the usefulness of molecular dynamics simulations to impact drug and vaccine design by increasing

A Second-Generation Oral SARS-CoV‑2 Main Protease Inhibitor Clinical Candidate for the Treatment of COVID-19   At the start of the decade, amidst the SARS-CoV-2 pandemic, there was a huge amount of interest in the rapid development and delivery of antiviral therapeutics to those in need. Among others,[ doi.org/10.1126/science.abo7201] Pfizer rose to this challenge with a

Deriving general structure–activity/selectivity relationship patterns for different subfamilies of cyclin-dependent kinase inhibitors using machine learning methods Kaveh, S. et al. Deriving general structure–activity/selectivity relationship patterns for different subfamilies of cyclin-dependent kinase inhibitors using machine learning methods. Sci. Rep.14, 15315 (2024). doi.org/10.1038/s41598-024-66173-z The importance of explainable AI in guiding medicinal chemistry design is becoming increasingly evident.

Modelling P-gp efflux may help us design better molecules   In order to treat disorders of the central nervous system (CNS), it is essential to design molecules that can cross the blood-brain barrier (BBB), a layer of tightly packed endothelial cells rich in P-glycoprotein (P-gp) efflux pumps that protect the CNS from circulatory chemicals. Designing

The Importance of Tracking "Missing' Metabolites: How and Why? Throughout the course of in-vivo studies, the task of tracking where a drug goes and what it does after entering a living system is, of course, a routine activity for Medicinal Chemists and DMPK Scientists. The data generated will inform the likely outcome of drug dosing

Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit This useful perspective article discusses many recent attempts at applying machine learning strategies (particularly transfer and active learning) to reaction improvement and discovery. The authors present many of the examples where researchers have sought to apply these methods and achieve a balanced perspective on the

Discovery of an Oral, Beyond-Rule-of-Five Mcl‐1 Protein−Protein Interaction Modulator with the Potential of Treating Hematological Malignancies. In this paper, researchers from Janssen Pharmaceuticals were searching for a bespoke inhibitor of myeloid cell leukemia 1 (Mcl-1), which is among a family of proteins that regulate apoptosis, and is over-expressed in a number of hematological cancers and

    A Review of Computational Approaches Targeting SARS-CoV-2 Main Protease to the Discovery of New Potential Antiviral Compounds   Current Topics in Medicinal Chemistry, 2023, 23, 3-16   For this month’s computational chemistry paper of the month, we have chosen to highlight a recent review of the computational approaches used to identify inhibitors of

Recent bRo5 publications Figure 1: Picture summarising the limitations of obeying the Ro5 and the versatility obtained by going beyond (www.sciencedirect.com/science/article/abs/pii/S1367593117300480?via%3Dihub). Cast our minds back... It’s been more than 25 years since a Pfizer chemist: Christopher Lipinski, published1 his influential analysis of the calculated physical properties of a set of candidate drugs, leading to the well-known