BiopharmaTrend.com

A fresh viewpoint on drug discovery, pharma, and biotech

Subscribe | Become an author | Author LogIn

White Papers And Industry Reports

Trends, opportunities and the future of drug discovery

2018: AI Is Surging In Drug Discovery Market

   by Andrii Buvailo    3946
2018: AI Is Surging In Drug Discovery Market

The idea of using artificial intelligence (AI) to accelerate drug discovery process and boost a success rate of pharmaceutical research programs has inspired a surge of activity in this area over the last several years. In 2018, things are getting even “hotter” with the increase in the amount of partnerships, investments and other important events, summarized and grouped below into “mini-trends”.

Artificial Intelligence For Drug Discovery Use Cases At Mind the Byte

   by David Vidal    1535
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    2080

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    1636

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.

3 Ways Big Data and Machine Learning Revolutionize Drug Discovery

   by Andrii Buvailo    7472

The Internet media is trending now with numerous mentions of “big data”, “machine learning” and “artificial intelligence” all together destined to revolutionize pharmaceutical and biotech industries and the way drugs are discovered. These new technologies are believed to make drug discovery cheaper, faster, and more productive.

But how is “magic” supposed to happen, after all?