The course describes computational SBDD workflows aimed at advancing medicinal chemistry campaigns. Using real examples, attendees will learn how to search databases of structural families to analyse protein-ligand interaction patterns and use these, and pharmacophore constraints, to guide docking.

The course demonstrates how Lead Optimization can be advanced by performing scaffold replacement and medchem transformations on a ligand within the context of a receptor pocket. Transformations sourced from real chemical reactions are used alongside structural constraints to produce libraries of compounds that may enhance binding.

A fragment hit can be an effective chemical starting point for a drug discovery project. Our ability to find fragment hits and evolve them into leads could potentially be improved by enhancing our understanding of their binding properties, which to date has largely been based on tacit knowledge and reports from individual projects. An analysis of the molecular and binding properties of fragment hits and leads is presented with special focus on features that these hits and leads tend to have in common, as well as on properties where clear differences occur. Taking account of these preferences in designing and selecting fragments to screen, and in evolving fragments to leads may increase the chances of success in fragment-based drug discovery campaigns.

The complement pathway is an important part of the immune system, and uncontrolled activation is implicated in many diseases. The human complement component 5 protein (C5) is a validated drug target within the complement pathway, as an anti-C5 antibody (Soliris) is an approved therapy for paroxysmal nocturnal hemoglobinuria. Here, we report the identification, optimization and mechanism of action for the first small-molecule inhibitor of C5 complement protein.

It has been hypothesized that selective muscarinic acetylcholine receptor (mAChR) M4 subtype activation could provide therapeutic benefits to a number of neurological disorders while minimizing unwanted cholinergic side effects observed due to nonselective mAChR activation. Given the high sequence and structural homology of the orthosteric bind-ing sites among mAChRs, achieving M4 subtype-selective activation has been challenging. Herein, we describe the discovery of a series of M4 subtype-selective agonists bearing novel carbamate isosteres. Comparison of the isosteres’ electrostatic potential iso-surface sheds light on key structural features for M4 subtype-selective activation. The identified key features were further illustrated in a proposed receptor-agonist interaction mode.

Targeted protein degradation utilizing bivalent small molecules (PROTACs) represents a new modality in drug development, with numerous advantages vis-à-vis traditional inhibitor design. However, numerous challenges exist in PROTAC development, particularly concerning the rational design of efficacious molecules. In this presentation, multiple computational methods that enable the a priori evaluation of putative PROTAC molecules will be discussed. Numerous case studies will be offered, where the application of these computational tools can successfully recommend both potent PROTAC candidates and molecules to avoid. Results from scenarios across different target proteins, E3 ligases, and PROTAC architectures will be presented.

We describe a structure-based scaffold-hopping strategy for the design of PI3Kδ inhibitors. Starting from known compounds, we used an iterative scaffold-hopping workflow to generate chemically diverse compounds and optimize for potency and isoform selectivity. As a result, we found that an oxindole-based scaffold could be combined with multiple hinge binding motifs to generate inhibitors with exquisite selectivity.

TRPA1 is a non-selective cation channel for which there has been significant interest across multiple therapeutic areas due to literature describing its role in pain, asthma, and cancer pathways. Reports of pharmacological blockade of TRPA1 leading to efficacy in well-established models of neuropathic pain stimulated our interest in developing a TRPA1 antagonist for the treatment of chronic pain, however the tool compounds available for target validation were limited by potency and/or poor pharmacokinetics. Ligand-based optimization of a quinazolinone-based series of TRPA1 antagonists led to the discovery of the first potent, selective antagonist of TRPA1 with good physicochemical properties and pharmacokinetics enabling high target coverage in rats. AM-0902 demonstrates potent in vivo activity and has served as a valuable tool for the target validation efforts of multiple groups.

Janus kinases are a family of four enzymes; JAK1, JAK2, JAK3 and tyrosine kinase 2 (TYK2) that are critical in cytokine signaling. Overall, up to 70% of human tumors are linked to persistent elevated STAT3 activity which can be associated with poorer prognosis in many of these settings. JAK1 is believed to be a primary driver of STAT3 phosphorylation and signaling. JAK2, however, is essential for signal transduction downstream of erythropoietin, thrombopoietin and other related receptors that control erythrocyte and megakaryocyte expansion. Therefore a JAK1 selective inhibitor may enable higher target coverage of the JAK1-STAT3 signaling by sparing toxicities such as thrombocytopenia and anemia, associated with JAK2 inhibition, to maximize the clinical benefit.

We will present our detailed medicinal chemistry optimization using structure-based design from a non-kinome selective screening hit to the candidate drug AZD4205, a highly selective novel JAK1 kinase inhibitor. AZD4205 displayed high levels of JAK1 biochemical and cellular potency with favorable physical properties and excellent preclinical in vivo pharmacokinetics. AZD4205, in a combination with low dose osimertinib, demonstrated a greater antitumor activity in an H1975 (T790M) NSCLC xenograft model. These findings warrant the further clinical investigation of AZD4205 to fully understand its potential as an anti-cancer therapeutic.

In silico prediction models have been used for a long time in drug discovery. First were named as QSAR and QSPR models then renamed to machine learning models. As computers got faster and more data became available these models got more accurate and more useful for multiparameter optimization of compounds. These can be used as filters for prioritizing ideas for synthesis. There are several tools in MOE for generating ideas: MedChem transformations, BREED for hybridizing know actives, combinatorial builder for generating libraries. The final prioritization of ideas can be done using either ligand or structure-based methods. An example of how to use these methods will be shown.