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In Silico

Section: White Papers And Industry Reports     View all sections


Artificial Intelligence For Drug Discovery Use Cases At Mind the Byte

   by David Vidal    2498
Artificial Intelligence For Drug Discovery Use Cases At Mind the Byte

Artificial intelligence (AI) has become a hot topic in the biopharmaceutical environment and nearly every pharma company in the world has embraced it hoping that it will play a major role in speeding up drug discovery, by reducing R&D costs and avoiding failure in late development stages. According to prospects, AI-driven drug discovery will lead to the development of new and more effective drugs, paving thus the way to personalized medicine.

[White paper] High Throughput Quantum Chemistry for Drug Discovery - Towards Reaction Screening

   by Peter Jarowski    3662
[White paper] High Throughput Quantum Chemistry for Drug Discovery - Towards Reaction Screening

In the domain of drug discovery, there can be a world of difference between a computer-generated hit compound, which is predicted to bind well to a drug target and what can be reliably synthesized at scale, or indeed synthesized at all. This discrepancy has been a lingering point of discord between the Discovery and R&D efforts in the chemical industry. Computer-aided drug design (CADD) has become an increasingly valuable tool by providing essential screening data and unique insight into drug action and mechanism, but it does not model the more complex world of chemical reactivity and synthetic chemistry.

Genenerative AI Models In Small Molecule Drug Discovery: The Open Challenge To Create A Unified Benchmark

   by Mostapha Benhenda    3465
Genenerative AI Models In Small Molecule Drug Discovery: The Open Challenge To Create A Unified Benchmark

Generative AI models in chemistry are increasingly popular in the research community, mainly, due to their interest for drug discovery applications. They generate virtual molecules with desired chemical and biological properties (more details in this blog post).

However, this flourishing literature still lacks a unified benchmark. Such benchmark would provide a common framework to evaluate and compare different generative models. Moreover, it would help to formulate best practices for this emerging industry of ‘AI molecule generators’: how much training data is needed, for how long the model should be trained, and so on.