Accurate estimation of conformational strain in small-molecule ligands is critical for successful structure-based drug design. While torsional strain in acyclic bonds is readily assessed, evaluating strain in saturated ring systems currently necessitates conformational sampling and compute-intensive quantum-mechanical calculations. To address this limitation, RingAnalyzer is introduced, i.e., a machine learning framework predicting conformational strain directly from single three-dimensional molecular geometries. The method introduces a ring-centric 3D equivariant neural network trained on a rigorously curated dataset of 4,226 drug-like molecules and 27,581 conformations. Calculated at the high-fidelity $\omega$B97XD/Def2TZVP level of theory, this dataset captures diverse stereoelectronic effects. Following extensive data augmentation strategies yielding 76,636 conformations, the computational model accurately predicts strain energies with a mean absolute error of 0.54 kcal/mol, demonstrating robustness to structural artifacts and incomplete structures. Prospective application of RingAnalyzer across diverse structural repositories, including small-molecule conformer generators, 3D generative models, co-folding architectures, docking datasets, the Protein Data Bank, and the Cambridge Structural Database, highlights a widespread occurrence of high-energy ring artifacts. As a computationally efficient and quantum-accurate geometric filter, RingAnalyzer enables the rapid identification and elimination of strained ring systems to streamline structure-based molecular design.

Covalent drug discovery has matured into a quantitative discipline, with the clinical success of mutant-selective inhibitors validating a design philosophy that pairs reversible binding with a rationally tuned warheads. I will draw on recently disclosed real-world programs to highlight how warhead selection and tuning play out in practice. Starting with classical thiol-Michael chemistry, I will explore alternative cysteine-targeting modalities and discuss strategies for tuning warhead reactivity. Along the way, I will touch on covalency beyond cysteine and approaches in which the binding pocket itself shapes the covalent step.

Substituents, decorating the central molecule core, play a central role in medicinal chemistry, where they are modified during lead optimization to improve biological activity, physicochemical properties, selectivity and other drug properties. The selection of appropriate substituents, guided by the bioisosteric principle, is one of the most effective strategies for optimizing molecular properties while preserving affinity for the biological target. Despite the widespread application of bioisosteric replacement in drug discovery, systematic analyses of bioisosteric relationships among substituents derived from large collections of bioactive molecules remain limited. In this work, we present a comprehensive analysis of substituents occurring in bioactive molecules and investigate their bioisosteric relationships using data extracted from medicinal chemistry literature. Bioisosteric pairs are identified from substitution patterns observed in related compounds with activity on the same target, enabling the construction of a network of frequently interchangeable substituents. Because the analysis is based on the observed co-occurrence of substituents within the same molecular environments rather than on structural similarity or physicochemical descriptors, it captures experimentally validated substitution patterns and uncovers not only well-established bioisosteric pairs but also less well-known relationships. The resulting bioisosteric substituent network is made available through a free web application at https://ertlmolecular.com/tools.html , providing a practical resource for medicinal chemists engaged in lead optimization, analog design, and library development.

Medicinal chemistry decisions rarely rely on a single prediction. Instead, in a MedChem project one must balance potency, ADME, physicochemical properties, novelty, and project-specific constraints. To be impactful, AI/ML methods must therefore be integrated into decision-making workflows rather than used as standalone prediction tools.

Through examples from small-molecule drug discovery projects, I will show how generative chemistry, machine learning models for PhysChem and ADME properties, and multi-parameter optimization can be combined with structural and medicinal chemistry expertise to design, prioritize, and refine compounds. Particular emphasis will be placed on prediction reliability through uncertainty estimation and iterative model improvement. By integrating AI/ML approaches with medicinal chemistry expertise and structure- and physics-based methods, these workflows enable more informed compound prioritization, ultimately driving the identification of molecules with improved in vitro quality and more balanced target property profiles throughout the optimization process.

Traditional screening-based drug discovery is inherently limited by the astronomical scale of the underlying chemical space. Generative modelling offers a compelling alternative to the classical search paradigm and enables rational, bottom-up design of novel and target-specific small molecules. However, its impact has been hampered by challenges in synthetic accessibility of the designed compounds and lack of large-scale experimental validation. Here, we introduce LDDM (Large Drug Discovery Model), a generative framework that supports a wide range of drug discovery tasks, including constrained and unconstrained docking, fragment linking and growing, and de novo design. We further introduce a programmable design algorithm that enables accurate and synthesizable design of compounds satisfying various fine-grained objectives. We experimentally validated optimized and de novo designed peptide and small-molecule binders for seven therapeutically relevant proteins: CDO1, cathepsin S, Pin1, PGK1, KRAS, BRD4, and SARS-CoV-2 Nsp3. The best designs were structurally characterized through competition experiments, NMR spectroscopy, and X-ray crystallography, demonstrating high 3D modelling accuracy. Overall, LDDM provides a scalable and flexible platform for the rapid and tailored design of small molecules and non-natural peptides for diagnostic and therapeutic applications.

Snakebite envenoming is almost exclusively treated with cold-chain-dependent polyclonal antibody antivenom with limited cross-species coverage and frequent adverse reactions. Snake toxin families share highly conserved sequence identi ties with significantly different binding profiles, rendering sequence- and analog based drug design unfeasible. Here, we investigate whether binding-pocket geometry, independent of sequence and ligand similarity, constitutes a gen uine, exploitable design axis for the rapid discovery of novel polyspecific low molecular-weight antitoxins. Using BEAR, a pharmacologically anchored repos itory comprising 12,358 binding pockets across 1,197 drug targets, we systemat ically compared pocket geometry against full-length sequence, domain-trimmed sequence, and ligand chemical space across the druggable proteome. These three modalities were largely non-overlapping. Within tight structural neigh borhoods, pocket geometry reliably recovered pharmacologically related protein pairs and uniquely captured such relationships across evolutionarily distant fam ilies where sequence similarity failed. Leveraging this principle, we deployed a pocket-conditioned generative model to design candidate antitoxins directly from venom toxin structures, entirely bypassing the need for existing chemical matter or homologous targets. These findings demonstrate that binding-pocket geometry exposes functional relationships invisible to both evolutionary lineage and ligand profiles, providing a structural foundation for de novo ligand design against orphan targets in neglected global health applications.