Aurigene Pharmaceutical Services Limited (Aurigene), a contract research, development, and manufacturing services organisation and a Dr. Reddy’s Laboratories company, introduces Aurigene.AI, an AI and ML-assisted platform for accelerating drug discovery projects from hit identification to candidate nomination. Aurigene.AI combines advanced physics-based simulation, generative and predictive AI models, and CADD (Computer-Aided Drug Design) in one platform, allowing users to pick the appropriate algorithms for a given application. The modular platform also consists of a meticulously curated database of 180 million compounds and 1.6 million validated bioassay data points. This database is ever-expanding and serves as training data for the platform.
Aurigene.AI is hosted on Google Cloud, which offers a scalable infrastructure for handling large datasets and efficient computation while safeguarding client data confidentiality. Integrating AI and ML-based solutions with Aurigene’s core capabilities in chemical design, synthesis, and assessment in bioassays will facilitate faster development of novel therapeutics. Discovery scientists at Aurigene have validated the platform using a case study and demonstrated that the application of Aurigene.AI reduced the cycle time from chemical design to synthesis and testing by 35%.
Akhil Ravi, CEO, Aurigene Pharmaceutical Services Ltd., said, We are committed to continually advancing our service experience and providing innovative end-to-end solutions to our global partners. Aurigene.AI is an important addition to our small molecule discovery offering and represents a data-driven approach to novel therapeutic development. Dr. Gayathri Ramaswamy, Global Head of Discovery Services, Aurigene Pharmaceutical Services Ltd., said, The addition of AI and iterative machine learning capabilities to our core expertise in synthesizing and testing molecules will significantly reduce the DMTA cycle time in the discovery process. Aurigene.AI augments our core scientific capabilities in the small molecule discovery space and will serve as an efficient engine for identifying a drug candidate.
