Discovering new “drug-like” and synthetically viable compounds via artificial intelligence (AI) is of great interest to medicinal chemists.
One of the key challenges in using AI for drug discovery is the lack of robust machine learning (ML) models trained on experimentally proven data.
In this webinar, learn how experimentally validated AI/ML methods and computational tools of the AIDDISON™ integrated platform can solve the challenges in drug design for medicinal chemists.
Key Learning Objectives:
- Unlock ingenuity for new molecule design tailored to your needs with AI methods and computational tools
- Explore how AIDDISON can accelerate the process of hit identification and lead optimization
- Discover AIDDISON’s generative methods for designing novel “drug-like” and synthetically viable compounds
- Exploit machine learning toxicology models to guide pharmacokinetic profile search in ultra-large chemical spaces
Who Should Attend:
- Medicinal Chemists
- IT/Computational Chemists
- Discovery Scientists
- R&D Scientists
- Group/Team Leaders
- Director/VP-Level Executives
- Data Scientists
- Chief Scientific Officers