Where AI Should Actually Earn Its Keep in Biopharma - with Mukhtar Ahmed of Greenstone Biosciences

Daniel Faggella (0:12)

Welcome everyone to the Emerge AI in Business Podcast. Today's guest is Mukhtar Ahmed, Director of Project and Portfolio Strategy at Greenstone Biosciences. Mukhtar discusses why AI investment in Bioformer R&D has concentrated on generating more molecules than candidates, even though the industry's most expensive mistakes happen further downstream in deciding which programs deserve continued capital. He argues that AI's greatest value lies in synthesizing fragmented evidence across discovery, regulatory, and commercial teams, while keeping final judgment and accountability with human decision makers rather than the model. A quick note for our audience that the views and opinions expressed by Mukhtar on today's program are his own and do not reflect those of Greenstone Biosciences or its leadership. Emerj works with a select group of AI vendors to reach Fortune 500 decision makers through research, media, and direct access. If you want to be considered, download our media kit at emerj.com/ad1. That's emerj.com/ad1.

Now the conversation with Mukhtar.

Mukhtar, welcome to the Emerge AI in Business Podcast Studio.

Mukhtar Ahmed (1:41)

Thank you. Pleasure to be here.

Daniel Faggella (1:43)

I want us to start this conversation somewhere that might be a bit uncomfortable, because I think it's probably the honest place to start. When we look at how AI is being framed in biopharma right now, it's almost as if every story is about generating more, right? Like more molecules, more candidates, more targets. And yet, the thing the industry has never really been short of is compounds. So from your seat, when you look at where AI is being pointed inside R&D today, what stands out to you about where the spotlight is landing and where it isn't?

Mukhtar Ahmed (2:19)

That's a great question. I'll start with saying that I think we are focusing AI on the wrong part of the problem. Drug development is not only a scientific problem, it is also a capital allocation problem conducted under extreme uncertainty.

Biopharma does not primarily suffer from an inability to generate molecules. It suffers from an inability to know early enough which hypothesis deserves more capital. And that is where I think AI can create considerably more value. So, to your question, what stands out to me is how lopsided the investment has been. Most AI investments in biopharma is still concentrated at the molecule level, like you said, generative chemistry, structured prediction, virtual screening, target identification. That makes sense. These applications produce something tangible. You can show a molecule, you can show a docking score, or show that screening time fell by 50 percent. What is much harder to show is a bad program that was never started, or an asset that was killed two years earlier, because the evidence simply wasn't strong enough. And yet, economically, those may be some of the most valuable decisions AI can help us make. And I think there is an assumption underneath much of the current AI investment, that if we can generate more candidates and make more shots on goal, we will improve our probability of success. But that only works if we are equally good at distinguishing the strong shots from the weak ones. Otherwise, AI can make the discovery engine faster, while simply allowing us to pursue weak hypotheses more efficiently.

And in biopharma, the most expensive mistake isn't moving slowly. It's moving very quickly around the wrong asset. I think an example, an interesting example, is the DSP 1181, that Accentia and Sumitomo reportedly moved from molecule, that molecule from project initiation to phase one trial in roughly 12 months. That was an extraordinary demonstration of what AI could do to compress part of the discovery process.

But the program was subsequently discontinued. And I don't think the lesson is that AI failed. It did what it was designed to do. It accelerated part of the discovery. The larger question is whether we are applying the same sophistication to deciding which programs deserve another $10 million, another two years, and another piece of organizational attention. And you see, one week portfolio decision doesn't just consume capital. It can crowd out three better opportunities that never receive the same resource.

So when I look at AI in biopharma today, I see tremendous sophistication being applied to generating and optimizing candidates. The next frontier is applying that sophistication to judgment, which hypotheses deserve more programs, which programs deserve more capital, and which ones should have the discipline, we have the discipline to stop.

Daniel Faggella (6:05)

It sounds like the bigger problem is if we had to put it in the plainest of terms, that we're pointing AI at the wrong bottlenecks. And I mean, you've been in this space for, I think, more years than what you would want our audience members to know. We don't want to give away your age. But is this pattern of pointing AI at the wrong bottleneck new, or is it just the latest version of something that you've seen before?