Rethinking drug design in the artificial intelligence era.

Artificial intelligence (AI) tools are increasingly being applied in drug discovery. While some protagonists point to vast opportunities potentially offered by such tools, others remain sceptical, waiting for a clear impact to be shown in drug discovery projects. The reality is probably somewhere in-between these extremes, yet it is clear that AI is providing new challenges not only for the scientists involved but also for the biopharma industry and its established processes for discovering and developing new medicines.

Learning Medicinal Chemistry Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) Rules from Cross-Company Matched Molecular Pairs Analysis (MMPA).

The first large scale analysis of in vitro absorption, distribution, metabolism, excretion, and toxicity (ADMET) data shared across multiple major pharma has been performed. Using advanced matched molecular pair analysis (MMPA), we combined data from three pharmaceutical companies and generated ADMET rules, avoiding the need to disclose the full chemical structures. On top of the very large exchange of knowledge, all companies involved synergistically gained approximately 20% more rules from the shared transformations.

Outcome of the First wwPDB/CCDC/D3R Ligand Validation Workshop.

Crystallographic studies of ligands bound to biological macromolecules (proteins and nucleic acids) represent an important source of information concerning drug-target interactions, providing atomic level insights into the physical chemistry of complex formation between macromolecules and ligands. Of the more than 115,000 entries extant in the Protein Data Bank (PDB) archive, ∼75% include at least one non-polymeric ligand. Ligand geometrical and stereochemical quality, the suitability of ligand models for in silico drug discovery and design, and the goodness-of-fit of ligand models to electron-density maps vary widely across the archive.

Fragment-based discovery of potent ERK2 pyrrolopyrazine inhibitors.

A fragment-based lead discovery approach was used to discover novel ERK2 inhibitors. The crystal structure of N-benzyl-9H-purin-6-amine 1 in complex with ERK2 elucidated its hinge-binding mode. In addition, the simultaneous binding of an imidazole molecule adjacent to 1 suggested a direction for fragment expansion.

An integrated suite of modeling tools that empower scientists in structure- and property-based drug design.

Structure- and property-based drug design is an integral part of modern drug discovery, enabling the design of compounds aimed at improving potency and selectivity. However, building molecules using desktop modeling tools can easily lead to poor designs that appear to form many favorable interactions with the protein's active site. Although a proposed molecule looks good on screen and appears to fit into the protein site X-ray crystal structure or pharmacophore model, doing so might require a high-energy small molecule conformation, which would likely be inactive.

Discovery of potent and selective pyrazolopyrimidine janus kinase 2 inhibitors.

The discovery of somatic Jak2 mutations in patients with chronic myeloproliferative neoplasms has led to significant interest in discovering selective Jak2 inhibitors for use in treating these disorders. A high-throughput screening effort identified the pyrazolo[1,5-a]pyrimidine scaffold as a potent inhibitor of Jak2. Optimization of lead compounds 7a-b and 8 in this chemical series for activity against Jak2, selectivity against other Jak family kinases, and good in vivo pharmacokinetic properties led to the discovery of 7j.

SGX523 is an exquisitely selective, ATP-competitive inhibitor of the MET receptor tyrosine kinase with antitumor activity in vivo.

The MET receptor tyrosine kinase has emerged as an important target for the development of novel cancer therapeutics. Activation of MET by mutation or gene amplification has been linked to kidney, gastric, and lung cancers. In other cancers, such as glioblastoma, autocrine activation of MET has been demonstrated.

Fragment-based discovery of hepatitis C virus NS5b RNA polymerase inhibitors.

Non-nucleoside inhibitors of HCV NS5b RNA polymerase were discovered by a fragment-based lead discovery approach, beginning with crystallographic fragment screening. The NS5b binding affinity and biochemical activity of fragment hits and inhibitors was determined by surface plasmon resonance (Biacore) and an enzyme inhibition assay, respectively. Crystallographic fragment screening hits with approximately 1-10mM binding affinity (K(D)) were iteratively optimized to give leads with approximately 200nM biochemical activity and low microM cellular activity in a Replicon assay.