Drug-Induced Liver Injury: A Medicinal Chemist’s Perspective

Drug Hunter has sifted through news articles, press releases, financial disclosures, SEC filings, and more to bring you a roundup of 2023’s biggest mergers and acquisitions (M&A) in the drug discovery world. Following our open-access industry overview, in the final installment of this four-part series we bring you the biologics-focused deals opening checkbooks in 2023.

Imagine having access to an almost infinite amount of chemical space that could include the next blockbuster medicine waiting to be discovered. The challenge for medicinal chemists is balancing the exploration of this vast space in early drug discovery with the need for focused exploration in later stages. This article explores the genesis of a new AI-powered drug design tool, AIDDISON™ from MilliporeSigma, and how this platform can benefit discovery teams around the world.

The final set of Molecules of the Month for 2023 is out, and now it’s time to nominate your favorite molecules for 2023’s Molecules of the Year! Nominees should have either been first disclosed in 2023 or had a major new update in 2023 (e.g. clinical trial readout, publication, business transaction, approval). The molecule must have a [...]

PRMT5 is an epigenetic “synthetic lethality” target that has attracted much attention among drug hunters. The first generation of PRMT5 inhibitors was limited by systemic toxicities resulting in a cooling of industry interest, until the recent identification of tumor-specific inhibitors. These second-generation compounds target the MTA:PRMT5 complex in MTAP-deleted cancers—15% of all tumors—leading to a revival of the target. Read on to find out which companies are prevailing in the search for a first-in-class PRMT5 inhibitor and how their clinical compounds differentiate.

One of the key challenges in CNS projects is achieving sufficient unbound brain exposure to engage the target. To predict CNS exposure, scientists employ metrics like CNS MPO scores from Pfizer and Merck, Bayesian models, and methods for balancing physicochemical properties. However, these in silico metrics often fail to correlate well with experimental data. Schrödinger's team has discovered the energy of solvation (E-sol) metric, which accurately predicts Kp,uu and has been validated with CNS-penetrant DLK inhibitors. Explore more in this detailed Drug Hunter article.