Drug discovery
A team of researchers from the University of Prince Edward Island is working with partners at the Research Institute of the McGill University Health Centre and Neurodyn Life Sciences Inc. to develop a novel therapy to slow or potentially halt the progression of amyotrophic lateral sclerosis (ALS). Led by Dr.
Drug discovery
The Indian Pharmaceutical Association's industry-academia meet 2025 brought together experts to discuss the transformative impact of artificial intelligence on the pharmaceutical sector. The meet highlighted AI's potential to significantly reduce the 10-15 year timeline typically required for drug discovery and development. Experts noted that AI enables
Drug discovery
Machine learning has been touted as a means to increase efficiency in the drug development pipeline by identifying high-quality compounds early on. However, its potential has been hampered by the "generalizability gap," where models fail to recognize chemical structures they were not exposed to during training. Vanderbilt University&
Alzheimer's disease
A team of researchers from the University of Cambridge has made significant progress in developing potent and selective inhibitors of the NUAK1 protein kinase, a key player in various cellular functions including cell proliferation, migration, and adhesion. This research, funded by Alzheimer's Research UK (ARUK) and the ALBORADA
Machine learning
Researchers from Hainan Medical University have made a significant contribution to the field of personalized medicine, leveraging machine learning algorithms to identify potential drug candidates for colon cancer. According to a new study, the team employed computational oncology to advance multi-targeted therapies for the disease, addressing challenges in understanding molecular
Machine learning
Circle Pharma, a clinical-stage biopharmaceutical company, has announced an agreement with Eli Lilly and Company to leverage Lilly's AI/ML platform, TuneLab, to enhance its MXMOtm platform in developing macrocycle therapeutics for difficult-to-treat cancers. This collaboration is part of Lilly's Catalyze360 model, which aims to empower
Machine learning
Insitro, a pioneer in machine learning for drug discovery and development, has announced a new collaboration with Eli Lilly and Company (Lilly) to develop advanced machine learning models that can predict key pharmacological properties of small molecules. This effort aims to address longstanding challenges in drug development where such properties
Machine learning
Eli Lilly has introduced Lilly TuneLab, a machine learning platform that allows selected early-stage biotechs to access drug discovery models trained on years of research data. The platform, hosted by a third party, employs federated learning to enable companies to take advantage of Lilly's AI models without sharing
Machine learning
Eli Lilly and Company announced the launch of Lilly TuneLab, an artificial intelligence and machine learning (AI/ML) platform that provides biotech companies access to drug discovery models trained on years of Lilly's research data. The platform is part of Lilly Catalyze360's offerings for early-stage biotech
Machine learning
As India's pharmaceutical sector prepares for a future of potentially $350 billion in exports by 2047, its emphasis on innovation has become crucial. The nation aims to transition from its role as the "Pharmacy of the World," focusing on manufacturing excellence to advancing into breakthrough innovation.
Machine learning
Research conducted by Merck & Company has led to a significant advancement in the field of medicinal chemistry, with the development of predictive models that enable faster discovery of safer and more efficacious therapeutics. The study highlights the potential of deep learning models, particularly graph neural networks, in improving model
Machine learning
The global drug discovery platforms market is undergoing significant expansion, driven by technological innovation and increasing research and development investments. Experts are leveraging high-throughput screening, AI-driven modeling, and advanced microfluidics to accelerate candidate identification. By 2025, the market size is estimated to be valued at USD 211.3 Mn, with
