Goal: Discover a new antibiotic capable of killing drug-resistant bacteria.

Method: Use a deep learning model to analyze a vast library of chemical compounds and predict antibacterial activity.

Process: The AI algorithm screened over 100 million chemical structures and identified a promising candidate. Researchers then tested this compound in the laboratory and in mouse models.

Result: A new antibiotic, named Halicin, was discovered. It successfully kills multiple strains of multi-drug-resistant bacteria and represents a new class of antibacterial compounds.

Goal: Discover a new antibiotic capable of killing drug-resistant bacteria.

Method: A deep learning model was used to analyze a vast library of chemical compounds and predict antibacterial activity.

Process: The AI algorithm screened over 100 million chemical structures and identified a promising candidate. Researchers then tested the compound in vitro and in mouse models.

Result: A novel antibiotic, named Halicin, was discovered. It effectively kills multiple drug-resistant bacterial strains and represents a new class of antibacterial agents.

Goal: Develop a new drug for idiopathic pulmonary fibrosis (IPF) using AI.

Method: An integrated AI platform was used to identify a novel disease target and generate an active compound against it.

Process: AI algorithms analyzed large omics datasets and identified the kinase TNIK as a promising target. A generative model then designed multiple novel molecules, from which a lead candidate was optimized. Target discovery and compound advancement to the preclinical stage were completed in just 18 months.

Result: An experimental drug (later named Rentosertib) was developed, successfully passing preclinical studies and entering clinical trials—making it one of the first AI-discovered medications of the modern era.

Goal: Automate and accelerate the design–make–test–learn (DMTL) cycle in drug discovery by integrating AI with robotic laboratories.

Method: Develop an AI-driven platform where generative AI designs candidate molecules and automated labs synthesize and biologically test them with minimal human intervention.

Process: Exscientia implemented an iterative DMTL workflow on a cloud infrastructure. AI continuously proposes novel molecules, while robotic systems synthesize and assay them 24/7. This approach:
- Reduced the number of compounds needed to identify a viable candidate by 10× vs. traditional methods.
- Accelerated drug design by ~70%, compressing years of work into months.

Result: The AI-automation synergy boosted R&D efficiency:
- Design cycles sped up by up to 70%.
- Capital costs cut by 80%.
- Multiple AI-designed candidates advanced to clinical trials.

This breakthrough demonstrates how "self-driving labs" powered by AI can transform pharmaceutical innovation.