10 Million Molecule Screening in 1 Week

As a CSO, you're racing against time, capital constraints, and the weight of bringing transformative science to patients who need it. Every month lost to computational bottlenecks is a month your competitors gain ground. Pauling.AI eliminates these bottlenecks for you. Our state-of-the-art AI agents have delivered validated hits to dozens of academic labs and biotech companies, letting you focus on bringing your science to the world. We handle the infrastructure, the computational chemistry expertise, and the 24/7 execution. You get results.

Hit Screening for Drug Discovery

Hit screening is the critical step between identifying a possible target for a disease or indication and developing a lead compound for that target. This phase evaluates large compound libraries against target structures to identify molecules with probable activity, typically estimated binding affinity. Success at this stage directly determines the quality of your pipeline and the probability of clinical success downstream.

The Role of Hit Screening in Modern Drug Discovery

Traditional high-throughput screening campaigns can evaluate millions of compounds but require significant capital investment, time, and physical infrastructure. The hit rate from experimental screens typically ranges from 0.01% to 0.1%, meaning thousands of compounds must be tested to identify viable starting points.

Computational hit screening transforms this paradigm by enabling rapid, cost-effective evaluation of vast chemical space before committing to wet-lab validation. Modern structure-based virtual screening can process millions of compounds in days rather than months, with hit rates often exceeding 5% when properly validated.

The quality of hits emerging from this stage is paramount. A hit must demonstrate not only target engagement but also sufficient chemical tractability for optimization. Poor hits lead to failed programs, wasted resources, and delayed timelines. Conversely, high-quality hits with favorable physicochemical properties and clear structure-activity relationships accelerate lead optimization and increase the probability of reaching clinical candidates.

Hit Screening with Pauling.AI

Our platform prioritizes in-stock compound libraries to eliminate procurement delays and accelerate validation timelines. By focusing on commercially available molecules, we compress the path from computational hit to experimental confirmation, enabling rapid iteration cycles that would be impossible with custom synthesis.

Pauling.AI combines three industry-standard computational methods into an integrated workflow. Physics-based molecular docking identifies binding poses and estimates binding affinity using established force fields. Machine learning models predict ADMET properties across 80+ endpoints, filtering molecules with unfavorable pharmacokinetic profiles before resource-intensive validation. Molecular dynamics simulations with mmPBSA free energy calculations provide rigorous thermodynamic assessment of protein-ligand stability.

This multi-method approach achieves prediction accuracy comparable to the most computationally expensive techniques while maintaining the speed and cost efficiency of simpler methods. By orchestrating these complementary analyses in parallel and applying intelligent ranking algorithms, we deliver high-confidence hits that balance binding affinity, drug-likeness, and synthetic accessibility without the traditional trade-off between accuracy and throughput.