AI in antibody drug discovery as a tool, not a magic wand

Early in the pandemic, AbCellera entered into a partnership with Lilly to co-develop antibody therapies for COVID-19. The pact eventually led to the development of a number of antibodies, including bamlanivimab, etesevimab and bebtelovimab. Shown here is the antibody bebtelovimab binding to the SARS-CoV-2 spike protein, highlighting key mutations from the omicron variant. The green structures represent the target-binding fragments (Fabs) of bebtelovimab, while the purple structure show the virus’s spike protein, with omicron mutations highlighted in red.

AI in drug discovery is a topic that gets an outsized amount of attention, observes Carl Hansen, CEO of AbCellera, a company specializing in antibody drug discovery. “It’s as if people are saying, ‘AI is here, it’s going to save us. Thank God, we’re finally gonna be able to create drugs,” he said. “To me, that’s implicitly …

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Why AI alone won’t resolve drug discovery challenges

A digital representation of a protein structure. [Image courtesy of NIH]

Big Pharma and researchers are sharpening their focus on AI to speed drug discovery. But the path to fully AI-driven drug discovery faces substantial hurdles, according to Adityo Prakash, CEO of Verseon. “When it comes to drug discovery, AI has a data problem with which the pharmaceutical industry has not yet come to terms,” he said.

Prakash explained there is simply “not enough data” to rely on AI as the primary means of small molecule drug discovery. He discussed these challenges further in an article in American Pharmaceutical Review titled “Exploring New Chemical Space for the Treatments of Tomorrow.”

The limitations and challenges of AI-first methods

Even with high-throughput screening to automate the process of testing pre-synthesized drug candidates against disease-associated target proteins, the pharma…

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Using AlphaFold, Insilico Medicine produces AI drug discovery in record time

This is the AI-powered autonomous robotics lab called Life Star in Suzhou that Insilico opened in January. [Insilico Medicine]

Capitalizing on AI drug discovery, an international group of researchers employed DeepMind’s deep learning-driven AlphaFold protein structure database to swiftly design and synthesize a potential hepatocellular carcinoma (HCC) drug in only 30 days.

The AI drug discovery project consisted of Insilico Medicine, the University of Toronto’s Acceleration Consortium and researchers including Nobel laureate Michael Levitt. The team applied AlphaFold to Insilico’s end-to-end AI-powered drug discovery platform, Pharma.AI. With the integration of the biocomputational engine PandaOmics and the generative chemistry engine Chemistry42, the AI drug discovery project identified a novel treatment pathway for HCC and developed a potent inhibitor based on a predicted protein structure. Read more

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Using AlphaFold, Insilico Medicine produces AI drug discovery in record time

This is the AI-powered autonomous robotics lab called Life Star in Suzhou that Insilico opened in January. [Insilico Medicine]

Capitalizing on AI drug discovery, an international group of researchers employed DeepMind’s deep learning-driven AlphaFold protein structure database to swiftly design and synthesize a potential hepatocellular carcinoma (HCC) drug in only 30 days.

The AI drug discovery project consisted of Insilico Medicine, the University of Toronto’s Acceleration Consortium and researchers including Nobel laureate Michael Levitt. The team applied AlphaFold to Insilico’s end-to-end AI-powered drug discovery platform, Pharma.AI. With the integration of the biocomputational engine PandaOmics and the generative chemistry engine Chemistry42, the AI drug discovery project identified a novel treatment pathway for HCC and developed a potent inhibitor based on a predicted protein structure. Read more

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AlphaFold: Redefining drug discovery with digital biology and AI

[Christoph Burgstedt/Adobe Stock]

At the Nvidia GTC 2023, DeepMind’s Founder and CEO, Demis Hassabis, provided an in-depth look into the seismic potential of their protein folding AI system, AlphaFold. Hassabis said AlphaFold was a contender for the organization’s “biggest project to date.”

Hassabis noted that DeepMind’s AlphaFold has made strides in addressing the protein folding problem, a challenge that has stumped scientists for more than five decades. The protein folding problem involves predicting the 3D structure of proteins solely from the amino acid sequence. Until AlphaFold, determining a protein’s structure was a complex and time-consuming process, often taking years of experimental work.

At the end of 2022, the company announced that it updated AlphaFold with the structure predictions of more than 200 million proteins.

Hassabis explained the enormity of the challenge, …

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7 case studies highlighting the potential of DeepMind’s AlphaFold 

[Image courtesy of DeepMind]

Alphabet subsidiary DeepMind has announced that its AI system AlphaFold has predicted the structure of more than 200 million proteins, representing almost every known cataloged protein. In addition, AlphaFold and its partner, the EMBL’s European Bioinformatics Institute, announced that the recent release expanded the database by more than 200 fold, from almost 1 million to more than 200 million structures.

“When we launched the database last July, it was sort of recognized as a pretty big leap forward for biology,” said Demis Hassabis, founder and CEO of DeepMind, in a press conference. The release last year included roughly 350,000 high-quality predictions, including all proteins in the human body.

The continued advances of AlphaFold make it possible to search for the 3-D structure of proteins “almost as easily as doing a keyword Google search,” …

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Drug discovery isn’t rocket science. It’s harder.

Early in my career, my manager used the phrase in the above headline to highlight the difficulty inherent in drug discovery. Over the ensuing years, I have seen that statement repeatedly confirmed by the brutal attrition in the discovery and development of new drugs. There are so many variables that can kill a drug discovery project — ranging from target validation and hit generation to off-target effects and formulation challenges — and that’s before even entering the clinic, where a whole new set of attrition factors arise. The number of variables to be simultaneously optimized is immense. One is never quite sure if it is even possible to thread the needle and arrive at a global optimum. It is a testament to the grit and persistence of drug discovery scientists that we have found as many lifesaving drugs as we have.

As a multiparameter optimization problem, drug discovery is perhaps the most challenging example we face. But recent advances in computational power and…

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