LABORATORY INFORMATICS
to ‘trying to fit a key into a lock with a lot of keyholes.’ Typical models time- consumingly score each ‘fit’ before choosing the best one. In contrast, EquiBind directly predicts the precise key location in a single step without prior knowledge of the protein’s target pocket, which is known as ‘blind docking’. Unlike most models that require several
attempts to find a favourable position for the ligand in the protein, EquiBind already has built-in geometric reasoning that helps the model learn the underlying physics of molecules and successfully generalise to make better predictions when encountering new, unseen data. The release of these findings quickly
before final approval from the US Food and Drug Administration (FDA). Moreover, 90 per cent of all drugs fail
once they are tested in humans due to having no or too many side effects. One of the ways drug companies recoup the costs of these failures is by raising the prices of the drugs that are successful. The current computational process
for finding promising drug candidate molecules goes like this: most state- of-the-art computational models rely upon heavy candidate sampling coupled with methods like scoring, ranking, and fine-tuning to get the best ‘fit’ between the ligand and the protein. Hannes Stärk, lead author of the paper and a first-year graduate student advised by Regina Barzilay and Tommi Jaakkola in the MIT Department of Electrical Engineering and Computer Science, likens typical ligand-to-protein binding methodologies
“This approach, which leverages information from thousands of publicly available crystal structures, has the potential to impact the field in new ways”
www.scientific-computing.com | @scwmagazine
attracted the attention of industry professionals, including Pat Walters, the chief data officer for Relay Therapeutics. Walters suggested the team try their model on an already existing drug and protein for lung cancer, leukaemia, and gastrointestinal tumours. Whereas most of the traditional docking methods failed to bind the ligands that worked on those proteins successfully, EquiBind succeeded. ‘EquiBind provides a unique solution to the docking problem that incorporates both pose prediction and binding site identification,’ Walters says. ‘This approach, which leverages information from thousands of publicly available crystal structures, has the potential to impact the field in new ways.’ ‘We were amazed that while all other methods got it completely wrong or only got one correct, EquiBind was able to put it into the correct pocket, so we were very happy to see the results for this,’ added Stärk.
While EquiBind has received a great deal
of feedback from industry professionals that has helped the team consider practical uses for the computational model, Stärk hopes to find different perspectives at the upcoming ICML in July. ‘The feedback I’m most looking forward
to is suggestions on how to improve the model further,’ he says. ‘I want to discuss with those researchers … to tell them what I think can be the next steps and encourage them to go ahead and use the model for their own papers and for their own methods … we’ve had many researchers already reaching out and asking if we think the model could be useful for their problem.’ This work was funded, in part, by the Pharmaceutical Discovery and Synthesis consortium; the Jameel Clinic; the DTRA Discovery of Medical Countermeasures Against New and Emerging threats program; the DARPA
“We strongly believe, after several decades of stagnated investment and innovation, cardiovascular disease is re-emerging with a newfound interest”
Accelerated Molecular Discovery program; the MIT-Takeda Fellowship; and the NSF Expeditions grant Collaborative Research: Understanding the World Through Code.
Cardiovascular disease CardiaTec Biosciences recently announced that it had secured a £1.4million pre-seed investment led by Laidlaw Scholars Ventures and APEX Ventures with participation from Crista Galli Ventures, o2h ventures and Cambridge Enterprise. The AI drug target discovery company, which specialises in cardiovascular disease, was co-founded in 2021 by a trio of AI academics and ambitious alumni from the University of Cambridge. Of the three – Raphael Peralta
(CEO), Thelma Zablocki (COO), and Namshik Han (CTO) – Raphael and Thelma are graduates of the University of Cambridge MPhil in bioscience enterprise.
Dr Han is an academic in AI
applications for target and drug discovery, and he holds positions at the University of Cambridge as head of AI, at the Milner Therapeutics Institute and associate faculty of the Cambridge Centre for AI in Medicine. The company is developing a target
discovery platform leveraging AI to make sense of large-scale multi-omic cardiovascular data. As opposed to conventional singular omic analysis, CardiaTec’s proprietary platform unravels relationships that span across every level of biology, from gene variation, methylation and expression, to their connection to proteomic and metabolomic functions to understand disease development best. Dr Han said: ‘Recent advances in
artificial intelligence are generating novel ways to interpret multi-omic data. I am excited to lead CardiaTec’s technology strategy to establish a new paradigm for understanding the pathophysiology of cardiovascular diseases.’ Raphael Peralta, CEO of CardiaTec, said: ‘We strongly believe, after several decades of stagnated investment and innovation, cardiovascular disease is
g Summer 2022 Scientific Computing World 17
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