Rajbanshi, B.; Iqbal, K.; Guruacharya, A.

Assessing whether a preclinical drug candidate will work is not a prediction problem but a reasoning problem. The same numerical output warrants different interpretations depending on the target and therapeutic context. CNS drug development presents the most demanding instance of this reasoning problem. For example, a compound must cross the blood-brain barrier, resist efflux transport, and achieve adequate receptor occupancy at a dose that clears safety margins. The constraints interact with each other in a web that needs careful interpretation. Here, we show that Cirrina, an LLM agent coupled to eight mechanistic pharmacology tools, can reason across the input data to provide better decisions and a well documented reasoning trace. The LLM agent reasons across multiple data tiers from SMILES to animal PK/PD measurements adjusting thresholds based on target-specific requirements. Validated against 181 CNS compounds, it achieved a 68% accuracy compared to a rule-based deterministic pipeline of 31% accuracy. In 103 discordant cases, the agent's reasoning was correct in 75% of instances compared to only 10% for deterministic pipelines. Cirrina provides a scalable, documented framework for preclinical decision-making, effectively identifying failure-prone candidates that generic thresholds overlook, and thereby reducing the chances of failure along the clinical development cycle.