Multiagent AI for Target Assessment: Turning Evidence into Decisions
Target identification sits at the front of every drug discovery program, and it remains one of the most consequential steps for downstream failure. Pick the wrong target—or fail to spot the right one—and no amount of downstream execution recovers the loss. Most drug programs fail not because of chemistry, but because the biological rationale was never sufficiently stress-tested.
The academic literature reflects genuine progress in how that rationale gets assembled. Public databases—STRING, DISGENET, the Human Protein Atlas—now provide dense, well-curated information on protein interactions, gene–disease associations, and tissue expression across the human genome. Recent computational tools such as TICTAC have demonstrated that clinical trial evidence, when combined with multi-source biological data and a rational scoring framework, can be used to systematically rank and prioritize therapeutic hypotheses at scale (Abok et al., 2025). LLM-based agents have gone further still, automating causal inference across the literature through Mendelian randomization without requiring manual curation at each step (Xu et al., 2025).
The problem is not the data. It is the absence of integration.
There is not a universal definition of what a “good” target looks like. A decade ago, Stock et al. showed that oncology programs were systematically over-selecting chemically tractable targets at the expense of targets with stronger disease biology—a bias that drove drop-out rates rather than reducing them (Stock et al., 2015). The field has moved on, but the structural issue has not: evidence lives across dozens of external databases and internal systems, is rarely weighted consistently, and is assessed by individual scientists working under time pressure rather than by reproducible, auditable workflows. Estimates of AI adoption in life sciences R&D still sit around 30%, not because the tools don’t exist, but because integration is where the work actually stops.
SigmaticOS addresses this directly. Rather than a single analytical tool, it provides an orchestration layer in which specialized agents work in concert—retrieving protein and interaction data, integrating gene–disease and variant evidence from DISGENET, computing composite druggability scores, and generating natural-language summaries—all within a single automated workflow. Every component is configurable: evidence weights, database selection, and proprietary internal datasets can all be connected to reflect each organization’s standards and priorities.
The result is a target assessment workflow that runs in minutes, returns structured and interpretable output, and scales across portfolios without growing the scientist burden. As work on LLM-agent systems in functional genomics has noted, the highest-value applications are not isolated prediction models but integrated, adaptive systems capable of coordinating analysis across the full experimental lifecycle (Zhao et al., 2026). Target assessment is a natural starting point—the decision with the longest shadow over everything that follows.
Want to learn more? Lets connect: https://meetings.hubspot.com/dcooney
References :
- Abok JI et al. “TICTAC: target illumination clinical trial analytics with cheminformatics.” Front Bioinform. 2025. https://doi.org/10.3389/fbinf.2025.1579865
- Xu W et al. “MRAgent: an LLM-based automated agent for causal knowledge discovery via Mendelian randomization.” Brief Bioinform. 2025. https://doi.org/10.1093/bib/bbaf140
- Stock JK et al. “Addressing the right targets in oncology: challenges and alternative approaches.” J Biomol Screen. 2015. https://doi.org/10.1177/1087057114564349
- Zhao Y et al. “AI-driven CRISPR screening: optimizing gene editing through automation and intelligent decision support.” J Transl Med. 2026. https://doi.org/10.1186/s12967-026-07849-0
