A state-aware functional classifier (SAFC) is developed that integrates molecular dynamics derived receptor ensembles, ensemble docking and protein ligand interaction graphs that provides dynamics-aware functional activity rankings for generated molecules that were partly complementary to docking, drug-likeness and synthetic accessibility scores.
Drug discovery remains constrained by high development costs (often exceeding USD 2.6 billion per approved drug) and long timelines (typically 10-15 years), with substantial late-stage attrition. We present Atomica, a web-based engineering-intelligence platform designed to integrate AI-driven molecular generation with reproducible cheminformatics validation and bioactivity-context retrieval in a single workflow. Atomica uses a server-mediated architecture to orchestrate MolMIM-based generation, descriptor and rule-based filtering, and PubChem record enrichment while avoiding client-side credential exposure. Molecule quality is assessed using explicit quantitative criteria, including validity, uniqueness, novelty, QED, synthetic accessibility (SA), LogP, and constraint success rate under user-defined similarity thresholds. The platform operationalizes constrained generation as a sampling-and-ranking process in descriptor space rather than a physics-based simulation, and it reports accepted versus rejected candidates to address invalid-output risk transparently. A representative end-to-end case workflow demonstrates practical candidate prioritization from seed structure input through filtering and evidence enrichment. Atomica is implemented with a typed, modular service layer and documented reproducibility controls (versioned dependencies, defined benchmarking protocol, and repeatable evaluation settings). This work contributes an integrated and scientifically auditable framework that bridges algorithmic molecular generation and applied, collaboration-ready drug-discovery workflows.
This review emphasizes the influence of new AI platforms like AlphaFold3, molecular interactions are structurally optimized (MISATO), and ZairaChem on the discovery of oncology drugs and examines how AI reconciles chemical design with pharmacological feasibility.
Mohsin Ali, Muhammad Ali Tajwar, Farid Ahmed et al.· Medicinal research reviews (...· 0 citations
Small-molecule drug discovery relies on iterative molecular optimization, where chemists repeatedly modify candidate compounds to balance multiple competing properties such as efficacy, toxicity, and solubility. Recent advances in generative AI (GenAI) have shown promise in accelerating this process by automatically proposing new molecular structures or targeted modifications. However, existing GenAI-based molecular design tools remain poorly aligned with experts'real-world workflows. Specifically, they offer limited support for specifying structure-level modification intents on molecules, provide insufficient transparency into model-generated modifications, and lack integrated support for downstream property evaluation with external computational tools. To address these challenges, we introduce MolecularCanvas, an interactive system that enables users to iteratively construct an optimization context by integrating high-level goals, structure-level annotations, property constraints, and reference-based preferences. This context guides the generation of candidate molecules across diverse molecular structures. MolecularCanvas further enhances transparency by providing evidence for AI-generated suggestions and streamlines molecular evaluation by integrating commonly used computational tools for property assessment into a unified interface. Finally, a user study with 12 participants demonstrates the usefulness and effectiveness of MolecularCanvas in helping users optimize candidate molecules.
Haoyu Dong, Rui Sheng, Shuhao Zhang et al.· 0 citations
This survey provides a comprehensive review of over 100 methods in 3D structure-based drug design (SBDD) and molecular optimisation, and proposes a unified taxonomy covering four primary generative paradigms: autoregressive models, diffusion models, flow matching, and Bayesian Flow Networks.