HYDERABAD: The opening day of BioAsia 2026 highlighted the transformative role of artificial intelligence in science and healthcare, alongside deliberations on scaling innovation, strengthening supply chains and mapping the pharmaceutical sector’s growth through 2030.
Global experts, scientists and industry leaders participated in four panel discussions on how technology, data and partnerships are reshaping life sciences.
In a plenary address, Google DeepMind vice-president (Science) Pushmeet Kohli said AI is accelerating discovery across biology, medicine, materials science and mathematics. Tracing DeepMind’s 15-year journey to “build AI to benefit humanity”, he described AlphaGo as a turning point that showed self-learning systems could surpass human expertise in complex tasks, paving the way for applications in scientific research.
He identified AlphaFold as the next major leap, aimed at solving the long-standing challenge of predicting a protein’s three-dimensional structure from its amino acid sequence. Since proteins underpin most biological processes, understanding their structure has far-reaching implications for disease research and drug discovery.
“In 2020, during the Covid-19 pandemic, AlphaFold2 achieved unprecedented accuracy in protein structure prediction. Predicted structures for nearly all known proteins are now publicly available, with more than three million researchers using the database. The breakthrough earned its key contributors the Nobel Prize in Chemistry,” he said.
Building on this, DeepMind introduced AlphaFold3, expanding predictions to interactions with DNA, RNA and small molecules, offering deeper insights into cellular biology and drug design.
Kohli also spoke about AI scientific agents, including an “AI co-scientist” capable of generating and refining research hypotheses. While stressing AI’s potential as a powerful knowledge accelerator, he cautioned that such tools must be deployed responsibly, with due regard to limitations and ethical concerns.
In a session titled “Catalysts that Actually Scale: Science, Software & Supply”, panellists said not every breakthrough reaches patients. Scalability, they noted, depends on integrating advanced science with digital technologies, automation and resilient supply chains from the outset. Early embedding of software and data analytics in the R&D cycle was described as crucial to moving innovation from lab to market.
Another session, “From Headwinds to Health Wins: Where Growth Will Come From (‘26-’30)”, examined pharma and biotech prospects between 2026 and 2030. Speakers cited pricing pressures, regulatory uncertainty and talent shortages as challenges but expressed optimism driven by innovation-rich pipelines, expansion into emerging markets, digital transformation and strategic collaborations.
Discussions also focused on how generative AI and advanced computational tools are reshaping target identification, process development, analytics and manufacturing readiness. Achieving scalable Chemistry, Manufacturing and Controls frameworks was termed essential for faster clinical translation and commercial viability. Contract research, development and manufacturing organisations were noted to be increasingly leveraging AI to improve efficiency and strengthen value chains.