AI is becoming more essential as scientific data is becoming more abundant and more accessible. In order to offer high-quality evidence-based solutions our AI models are trained across massive federated datasets that contain millions of data points. AI-based solutions can then be employed to augment the cognitive abilities of individual scientists and teams to enable them to make more informed decisions in drug design.
AI based approaches provide a new way to rapidly generate novel molecules. There has been a recent explosion in generative models using neural networks. Massive virtual libraries of compounds can be rapidly generated to explore new and exciting areas of chemical space.
Structure based drug design is heavily reliant upon high quality protein structures. Rapidly docking virtual molecules to proteins of interest is a computationally demanding problem.
High-throughput experimentation accelerates our chances for discovery. Highly automated systems require well integrated hardware and advanced AI solutions to be truly transformational. Systematically carrying out well designed experiments can generate very valuable data to validate and further refine AI models.
Designing optimal synthetic routes has been a long-standing grand challenge in Chemistry. A new wave of approaches has focused on fully data-driven AI based models. This circumvents the need for laborious manual encoding by domain experts.
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