Artificial intelligence (AI) is transforming drug discovery, but its implementation must be thoughtful and strategic, according to Krishna Bulusu, senior director in oncology data science at AstraZeneca.
Speaking at the ELRIG Drug Discovery meeting in London on 11 March 2025, Bulusu outlined how AI can improve efficiency, reduce costs, and support personalise medicine, but cautioned that its success depends on data integration, model explainability, and context-specific predictions.
“Accelerating drug discovery doesn’t just mean doing the same thing very, very fast. It means that we’re also going to do different things, and we’re going to do things more efficiently,” Bulusu said. He stressed that AI should not be applied indiscriminately but should be used to answer well-defined scientific questions.
One key example of how AstraZeneca is using AI role in drug discovery is the company’s collaboration with biological simulation company Turbine. The partnership, announced in January 2024, uses Turbine’s simulated cancer cell technology to model drug resistance mechanisms in haematological cancers.
By integrating public and proprietary data, the AI-driven model generates millions of simulations, predicting drug interactions and identifying potential combination therapies. “Now that is powerful, right?” Bulusu asked. “Because the scalability aspect – if I have to go to the lab and do this, it’ll cost me a lot of money.”
Unlike traditional lab-based research, which can be time-consuming and expensive, AI simulations allow researchers to explore complex biological pathways at a fraction of the time. The model provides quantitative insights into how different inhibitors impact cellular pathways, offering a new way to generate hypotheses and refine drug development strategies.
“From a big pharma perspective, this is great because I have a portfolio of drugs, and you’re telling me where to position my portfolio drugs, either as normal therapy or in combinations. That’s why we work very closely with Turbine on this,” Bulusu added.
The idea of AI-driven biological simulation is gaining traction across the industry. Demis Hassabis, CEO of DeepMind and co-creator of AlphaFold, has previously shared his vision for AI-powered virtual cells: “My dream is to eventually have virtual cells, like a simulation of a virtual cell. We’re maybe ten years away from that,” Hassabis said at the Financial Times Pharma and Biotech Summit in November 2024.
However, Bulusu underscored that AI’s success in drug discovery is not just about technological advancement but also about ensuring that models are interpretable, and predictions are relevant. “The value for AI, at least with the larger organisations in the past, is when a non-data scientist understands and appreciates them. And this doesn’t happen unless you’re working together.”
Investigating the scope of AI in drug discovery
Despite AI’s potential, significant challenges remain. Bulusu pointed to gaps in longitudinal patient data, biases in AI models, and the need for better early disease detection.
“AI needs to become a thought partner. And for that to happen, the confidence and trust in what we’re doing as data scientists needs to come through,” he said.
To address these challenges, Bulusu emphasised the need for improved data collection and integration, particularly capturing the full patient journey from preclinical to clinical stages. “We are very bad as a community at generating longitudinal data,” he said. Longitudinal data needs to be colletcted before preclinical and clinical research, but this is currently happening in reverse. This is important, because Bulusu said while “a patient’s journey is very important to capture, we’re just taking snapshots of it.”
Looking ahead, Bulusu emphasised the importance of starting with the right scientific question before selecting an AI model.
“From an AI perspective, value and impact is and will always be driven by starting with the right question and then asking, what’s the right model to answer that question. It’s never the other way around,” Bulusu concluded.
