Engineering Intelligence for Drug Discovery: Atomica as an AI-Powered Computational Platform for Real-Time Molecular Design and Bioactivity Analysis

Abstract

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.