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Trends, opportunities and the future of drug discovery

2018: AI Is Surging In Drug Discovery Market

   by Andrii Buvailo    6538
2018: AI Is Surging In Drug Discovery Market

Updated: 14.12.2018. Newly added content is marked in the text with "Update" sign.

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”.

Microsurgery Robot Market to Amass Considerable Returns from Hospital & Clinical Pursuits, Global Industry to be Characterized by Cutting-edge Technological Advancements

   by Global Market Insights    128
Microsurgery Robot Market to Amass Considerable Returns from Hospital & Clinical Pursuits, Global Industry to be Characterized by Cutting-edge Technological Advancements

The deployment of robotics in healthcare to perform complex surgeries with more precision and accuracy has indeed propelled microsurgery robot market trends. Over the last few years robotics is being incorporated prominently in gastrointestinal, gynecological, and urological surgeries, owing to the growing awareness among the healthcare service providers and patients about minimally invasive treatments. Many patients with terminal disorders such as endometrial cancer have also found to be inclined toward robotic techniques. The increasing use of such innovative and minimally invasive medical treatment facilities is slated to fuel microsurgery robot industry size.

Artificial Intelligence For Drug Discovery Use Cases At Mind the Byte

   by David Vidal    1713
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    2297

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    1868

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.