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.

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.