Theoretical Chemist Pratyush Tiwary Answers Questions About Artificial Chemical Intelligence
The College of Computer, Mathematical, and Natural Sciences hosted a Reddit Ask-Me-Anything spotlighting research on the role of artificial intelligence in drug discovery.
University of Maryland Millard and Lee Alexander Professor Pratyush Tiwary participated in an Ask-Me-Anything (AMA) user-led discussion on Reddit to answer questions about thermodynamics and the role of artificial intelligence in drug discovery on October 29, 2025.
A professor in UMD’s Department of Chemistry and Biochemistry and Institute for Physical Science and Technology (IPST), Tiwary also leads therapeutic drug discovery research at the University of Maryland Institute for Health Computing (UM-IHC).
Tiwary’s lab focuses on problems at the intersection of statistical mechanics, molecular simulations and artificial intelligence to develop new simulation methods that could help revolutionize drug design, yielding therapies that more efficiently target various diseases.
This Reddit AMA has been edited for length and clarity.
Was IBM's Watson the first instance of using AI for drug discovery?
IBM Watson was definitely one of the first. But in some form or another, I think a lot of companies have been using a form of AI (even if not by that name) for the last several decades. Most big pharma companies have a computational branch, which screens molecules on computers before putting them in the lab. They use different forms of data analysis methods, which are often not that far from modern-day AI.
How did you decide between taking up a faculty position in America versus in your home country? Do you see yourself going back to India?
My then-girlfriend, now ex-girlfriend (and wife) Megan, was in the U.S. when I was looking for faculty positions, and it just seemed like a better fit for both of us to stay in the U.S. However, I am closely connected with faculty in India, and I go back there at every possible opportunity.
What's your favourite nerdy in-joke in your field?
"The metadynamics free energy has converged."
Closely followed by: "This sampling method is collective-variable free."
How do you verify results that were generated using any form of AI?
Great question! There are at least two ways of verifying results. The first is to deploy the results in real-world settings. This could be experiments or physics-based simulations. Experiments can sometimes be slow, although companies like Lila Biosciences are trying to tighten the loop between AI and experiment-based validation. What my group and other companies, like Schrodinger, do is perform validation of AI through approximations to reality, such as molecular dynamics simulations.
The second approach is to ask AI to explain what it did. If you cannot make sense of how the AI got to a certain conclusion, then you are less likely to trust it, and vice versa.
Where do you see artificial intelligence playing the biggest role in drug research? And where is it being overhyped?
I see a big role in automating roles where a lot of data has already been collected, and we need to perform interpolation in that space. This could be, for example, generating the structure of a protein closely related to something that already exists in the PDB (Protein Data Bank). As this similarity starts to decrease, the trust in AI predictions should gradually decrease.
However, I do not see this to be the case with a hype: rigor ratio exceeding healthy amounts. As a community, we are now routinely trusting AI predictions without carefully checking whether the prediction domain has any overlap with the domain of training the AI. This comes up not just in protein structure prediction but also in all aspects of a drug discovery campaign, starting from lead optimization to looking up patient healthcare data.
This does not mean that AI can never be used outside its training domain. In fact, some of the most cutting-edge work in generative AI rigorously addresses the question of out-of-distribution generalization. As we keep investing in these efforts, hopefully, the hype: rigor ratio will move in the right direction.
In drug discovery, how far do you think current AI models can go without new experimental data? At some point, do simulations alone start reinforcing their own biases instead of finding truly novel compounds?
Recently, I had the fortune of being invited by PNAS editors to write a perspective on this very question. It's open-access, and I recommend reading it here. I also recommend reading this Atlantic article.
At the more philosophical level, we are our biases. This is reflected in the experiments we carry out, and sooner or later, it will also be reflected in AI methods and futuristic experiments to be carried out by humanoids that mix AI with natural intelligence. Thus, there will be a spectrum of biases that will keep getting reinforced.
Where will that take us? I wish I knew. Some of it might be novel, some of it might be garbage. Hopefully, it will be grounded in reality through experiments or physics, so we can keep reducing the garbage.
Like most people, I'm far more familiar with transformers as they apply to LLMs. Are you using transformers, graph neural nets, or some hybrid?
We are heavily involved with diffusion models. You can read about other methods my group and others use from this perspective I recently wrote.
Do you see a feasible feedback loop emerging where simulations and biological data iteratively inform and refine one another? Can artificial chemical intelligence frameworks readily incorporate this kind of experimental feedback?
I think involving experimental feedback is the next frontier, and a lot of companies are moving in the direction of Superintelligence. I am sure you have read about Lila, which is not the only one.
The whole idea there is to do AI and experimental feedback in the same lab in a high-throughput manner. In a certain way, my own lab is doing something similar by providing feedback through approximations to reality, i.e., physics-based simulations. This also connects to your question about the future of AI-driven simulations, where predictions are validated and refined quickly. My new center on therapeutics discovery at the Institute for Health Computing is aiming to address some of these questions.
