What’s the benefit of AI/ML for structure-activity relationship studies?

A conversation with Ben R. Taft, Ph.D., Senior Director of Chemistry, Via Nova Therapeutics

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With an ever-growing body of knowledge and rapidly advancing technologies, the pace of change in the drug discovery industry is fast. Yesterday’s challenges turn into today’s opportunities, which then become commonplace in the world of tomorrow.

We sat down with Ben R. Taft, Ph.D., who was serving as the Senior Director of Medicinal Chemistry at Via Nova Therapeutics, to discuss challenges and opportunities in structure-activity relationship (SAR) studies.

CAS: What has been the biggest change you’ve experienced in SAR studies since you entered the field?

CAS: How are AI and ML affecting drug discovery and SAR?

CAS: Does using AI and ML have benefits beyond speeding up your work?

CAS: What role does AI and ML play for the drug discovery chemist today?

CAS: What do you think is the biggest bottleneck in small molecule drug discovery?

CAS: Do you see any good solutions to this bottleneck?

CAS: Why develop small molecule therapeutics when we have so many other options available?

CAS: Can you share your mission at Via Nova Therapeutics?

CAS: If you had a magic wand to change anything in the drug discovery process, what would you change?

Ben Taft has been working as a medicinal chemist since 2011. After completing his postdoc, he joined Novartis, where he conducted discovery-phase research for oncology indications. While at Novartis, he transitioned to infectious disease drug discovery. He then joined Via Nova Therapeutics, a Novartis antiviral spinout founded by Don Ganem and Kelly Wong, when Novartis exited the infectious disease space.