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.

One Chiral Fingerprint To Find Them All Markus Orsi & Jean-Louis Reymond. J. Cheminform., 2024, 16:53 doi: 10.1186/s13321-024-00849-6 In Chemoinformatics there are a few problems that always rear their ugly heads and stereochemistry is one of them, even though many computational tools use fingerprints which generally do not consider this. Orsi and Reymond have created

Identification of Novel, Selective Ataxia-Telangiectasia Mutated Kinase Inhibitors with the Ability to Penetrate the Blood−Brain Barrier: The Discovery of AZD1390 Achieving CNS penetration, when desired, is a hugely important and sometimes very difficult hurdle to overcome. To do this, the Blood-Brain Barrier (BBB): with its unique lining of endothelial cells and its cocktail of efflux

The First ATP Competitive Type-III c-MET Inhibitor? In this paper, the target was c-MET, which is a receptor tyrosine kinase implicated in the development of tumour cells. Hyperactivation of c-MET is associated with poor prognoses in cancer patients, including tumour survival and metastasis. Existing inhibitors of c-MET are clustered into types, depending on their site

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

Where Old Meets New: Advancements in AlphaFold with Some Help From Boltzmann   With the news saturated by the opportunities presented by AI, it’s easy to assume that AI alone holds all of the answers to our problems in computer-aided drug discovery. Indeed, the release of the ground-breaking protein structure prediction tool AlphaFold2, was an

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