The application of a single model to bioisosteric fragment merging, dual-target design, selectivity engineering, and modality hopping without task-specific retraining is demonstrated and established interaction profiles as a chemotype-agnostic interface between structure hypotheses and molecular design.
Abstract
Three-dimensional intermolecular interactions govern molecular recognition and are fundamental to the pharmacological activity of small-molecule drugs. We propose that an interaction profile, comprising shape, electrostatics, and directional pharmacophores, is a sufficient and transferable design specification for molecular design. We introduce ShEPhERD–2, a 3D generative model that generates molecular structures conditioned on explicit interaction profiles. ShEPhERD–2 generates low-strain, drug-like molecules more efficiently and with greater fidelity to target interaction profiles than its predecessor while enabling precise control over molecular design through pharmacophore prioritization, substructure constraints, and composition of multiple interaction profiles. We demonstrate the application of a single model to bioisosteric fragment merging, dual-target design, selectivity engineering, and modality hopping without task-specific retraining. These results establish interaction profiles as a chemotype-agnostic interface between structure hypotheses and molecular design.
The ensemble-conditioned guidance framework is introduced, a framework which conditions 3D molecular generative models on both axes simultaneously and applies to two practical drug discovery tasks, dual-target binder design and active-state-selective agonist design, where conditioning on the additional state improves t...
Ross Irwin, Alessandro Tibo, Jon Paul Janet et al.· 1 citation
LDDM (Large Drug Discovery Model) is introduced, a generative framework that supports a range of drug discovery tasks, including constrained and unconstrained docking, fragment linking and growing, and de novo design, and a programmable design algorithm that enables accurate design of synthetically accessible compounds...
Ilia Igashov, Arne Schneuing, Adrian W. Dobbelstein et al.· bioRxiv· 0 citations
LinkLlama is presented, a fine-tuned Meta Llama 3 model that bridges the gap between text-based generation and 3D spatial awareness, offering a highly controllable and chemically robust framework to accelerate linker design and drug discovery in general.
Kun-Yang Sun, Ying-Ze Wang, Justin Purnomo et al.· Journal of Chemical Informat...· 0 citations
ChemTSv3 is introduced, an exploration framework with a flexible architecture that accommodates diverse design scenarios for adaptive molecular design, and shows that this flexibility enables efficient exploration across diverse design spaces, from drug-like small molecules to proteins.
Packora is presented, a flow-based generative model for molecular CSP that jointly predicts atomic coordinates and the lattice from molecular graphs that outperforms the baselines on both structure generation and ranking benchmarks.
Nayoung Kim, Kiyoung Seong, Sungsoo Ahn· 0 citations
Ourotide, a deep learning framework coupling conditional geometric flow matching with interface-aware affinity learning, outperforms generalist models in backbone accuracy, interface recovery, and affinity prediction and suggests that overcoming data scarcity in the physical sciences requires physics-guided data augmen...
Yu-Hang Shen, Jie Zhang, Zhou-Yue Wu et al.· bioRxiv· 0 citations
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