The average investment to bring a new drug to market surpassed $2.6 billion in 2024 [1]. Much of that cost comes from late-stage failures - compounds that looked promising in early screens but crashed on ADMET properties: poor absorption, rapid metabolism, cardiac toxicity, or simply impossible to synthesize at scale.
The ADMET Optimization Agent changes this equation. It learns structure-activity relationship (SAR) patterns from project chemistry to predict solubility, permeability, hERG risk, metabolic liabilities, and synthetic tractability - then proposes targeted structural modifications with mechanistic justification.
The Lead Optimization Bottleneck
During lead optimization, medicinal chemists modify hit compounds to improve bioavailability, solubility, and stability [2]. This traditionally involves months of design-make-test-analyze cycles. A chemist proposes a modification, synthesizes the compound, runs assays, and interprets results - often finding that fixing one property breaks another.
The "fail early, fail cheap" philosophy has driven adoption of computational methods to identify problematic compounds earlier [1]. But prediction alone isn't enough. Chemists need actionable recommendations: what specific structural change will improve solubility without introducing hERG liability?
Learning from Project Chemistry
The ADMET Optimization Agent doesn't rely solely on generic public datasets. It learns SAR patterns from your project's own chemistry - the specific scaffold, the substituents that worked, the modifications that failed.
This project-specific learning captures context that generic models miss. A methyl group might improve potency on one scaffold but tank solubility on another. The agent learns these nuances from your data, building increasingly accurate predictions as the project progresses.
Predicting What Matters
The agent evaluates compounds across key ADMET dimensions:
Solubility: LogP and TPSA predictions identify compounds likely to precipitate in formulation or fail dissolution testing.
Permeability: Predicted membrane penetration flags compounds that won't reach intracellular targets.
hERG Risk: Cardiac ion channel binding predictions catch potential QT prolongation liabilities before they derail development.
Metabolic Stability: Identification of soft spots - sites likely to undergo rapid CYP-mediated oxidation or glucuronidation.
Synthetic Tractability: Assessment of whether proposed analogs can actually be made in reasonable yield.
From Prediction to Proposal
Prediction without prescription leaves chemists where they started. The ADMET Optimization Agent goes further - it proposes specific structural modifications with mechanistic justification.
Agent-generated analogs with improved solubility and reduced hERG risk. Example structures are shown for demonstration only.
In this example, Compound C shows unfavorable properties (LogP: 1.02, TPSA: 37.8) and fails optimization criteria. The agent proposes Analog 1 and Analog 3 with improved profiles - lower LogP for better solubility and higher TPSA for reduced hERG risk. Each proposal includes the rationale: adding polar groups to reduce lipophilicity, introducing heteroatoms to increase topological polar surface area.
Mechanistic Justification
The agent doesn't just say "add a hydroxyl group." It explains why: the hydroxyl increases TPSA from 37.8 to 58.0, reducing passive diffusion into cardiac tissue while maintaining sufficient permeability for the target compartment. This mechanistic transparency helps chemists evaluate proposals and builds trust in the recommendations.
Accelerating the Cycle
Traditional hit-to-lead optimization requires 12-18 months of iterative synthesis and testing. The ADMET Optimization Agent compresses this timeline by reducing trial-and-error.
Instead of synthesizing 50 analogs to find 3 with acceptable ADMET profiles, chemists can prioritize the 10 most likely to succeed. Instead of discovering hERG liability after weeks of synthesis, they can avoid problematic scaffolds from the start.
The agent continuously learns from each experimental result, refining predictions and improving proposals as the project advances.
Integration with Medicinal Chemistry
The agent augments rather than replaces medicinal chemistry expertise. Experienced chemists bring intuition about synthetic feasibility, knowledge of privileged scaffolds, and understanding of target biology that no model fully captures.
The agent handles the computational heavy lifting - evaluating thousands of potential modifications against multiple ADMET endpoints simultaneously - freeing chemists to focus on creative design and strategic decisions.
References
[1] Drug Discovery Today. "Bridging data and drug development: Machine learning approaches for next-generation ADMET prediction." 2025. https://pubmed.ncbi.nlm.nih.gov/41052751/
[2] ADMET and DMPK. "Leveraging machine learning models in evaluating ADMET properties for drug discovery and development." 2024. https://pmc.ncbi.nlm.nih.gov/articles/PMC12205928/