Undated
Mukhtar Ahmed is Director of Project and Portfolio Strategy at Greenstone Biosciences.
“Today's guest is Mukhtar Ahmed, director of project and portfolio strategy at Greenstone Biosciences.”
Topic
Mukhtar Ahmed
17 facts from 1 episode, in date order. These are what speakers said on the shows, each linked to the moment it was said. They aren't independently verified.
Undated
Mukhtar Ahmed's views on the program are his own and do not represent Greenstone Biosciences or its leadership.
“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.”
Undated
Mukhtar Ahmed says most AI investment in biopharma is concentrated at the molecule level, in areas such as generative chemistry, structure prediction, virtual screening and target identification.
“Most A I investments in biopharma is still concentrated at the molecule level, like you said, generative chemistry, structure prediction, virtual screening, target identification.”
Undated
Mukhtar Ahmed cites molecule-level AI results such as a molecule, a docking score or a 50% drop in screening time as tangible outputs that are easy to show.
“You can show a molecule, you can show a docking score, or show that screening time fell by 50%.”
Undated
Mukhtar Ahmed says the TSP-1181 program showed AI could compress part of the discovery process but was later discontinued.
“That was an extraordinary demonstration of what A I could do to compress part of the discovery process, but the program was subsequently discontinued.”
Undated
Mukhtar Ahmed argues that generating more candidates improves the odds of success only if companies can also tell strong candidates from weak ones.
“if we can generate more candidates and make more shots on goal, we'll improve our probability of success. But that only works if we are equally good at distinguishing the strong shots from the weak ones.”
Undated
Mukhtar Ahmed says the evidence behind a go/no-go decision is spread across discovery, regulatory, strategy and outside-counsel teams.
“Biology sit with discovery, regulatory precedent sits with another team, competitive intelligence may sit with strategy, intellectual property may sit with outside counsel.”
Undated
Mukhtar Ahmed says organizations can allocate attention by volume, recency and internal influence rather than by the value of the evidence.
“Organizations can end up allocating attention around according to volume, recency, and internal influence rather than evidentiary value.”
Undated
Mukhtar Ahmed's scientific background is in GPCR signal transduction.
“My own scientific background was in G P C R signal transduction, and there is a useful analogy there.”
Undated
Mukhtar Ahmed uses cell signaling as an analogy: cells gain specificity by amplifying some signals and attenuating others, not by receiving less information.
“A cell does not achieve specificity by receiving less information, it achieves specificity by controlling which signals are amplified and which are attenuated.”
Undated
Mukhtar Ahmed says AI models are trained mostly on the documented scientific record, which contains publication bias and incomplete biology.
“Models are trained largely on the documented record, and that record contain publication bias and incomplete biology.”
Undated
Mukhtar Ahmed says a recommendation that no one is prepared to defend to an investment committee or board is an input, not a decision.
“A recommendation that nobody is prepared to defend in an investment committee or board meeting is not a decision, it's just an input.”
Undated
Mukhtar Ahmed says a decision to advance a candidate can commit roughly $10 to $20 million to early development.
“whether to go forward with that and spend the next ten to $20 million for early development.”
Undated
Mukhtar Ahmed says that in his experience most portfolio calls are still made by humans even when AI helps.
“While A I helps, so far, I've seen majority of the calls are made by humans.”
Undated
Mukhtar Ahmed says the strongest AI applications in portfolio work are focused tasks such as synthesizing the competitive landscape, mapping regulatory precedent, interpreting biomarker data and surfacing contradictions.
“The strongest applications are usually focused, synthesizing a competitive landscape, mapping regulatory precedent, interpreting biomarker data, and or surfacing contradictions in the evidence.”
Undated
Mukhtar Ahmed says the benefit of decision-layer AI is often counterfactual, such as a program that did not keep getting funded.
“With decision layer A I, the benefit is often counterfactual. It is the program that did not get funded for another two years.”
Undated
Mukhtar Ahmed proposes that the right KPI for AI in biopharma is how much uncertainty the organization removes by committing the next dollar, not how much content the AI generates.
“the right K P I is not how much content the A I generates, it is how much uncertainty the organization removes by committing the next dollar.”
Episodes