Professor David T. Jones FRS is one of the world’s leading figures in bioinformatics, computational structural biology, and artificial intelligence applied to biology. Over more than three decades, he has been at the forefront of using computational and machine-learning methods to extract biological insight from sequence, structure, and evolutionary data. Currently Professor of Bioinformatics at University College London, with appointments spanning Computer Science and Structural and Molecular Biology, he has played a defining role in shaping modern computational protein science and in establishing many of the foundations on which today’s AI-driven biology now rests.

Professor Jones’s work has repeatedly anticipated and enabled major advances in the use of AI and machine learning for biological discovery. Long before AI in the life sciences became a major international focus, he developed computational methods that transformed protein structure prediction from a largely manual or heuristic discipline into one increasingly driven by statistical learning, sequence analysis, and predictive modelling. His early development of threading methods for protein fold recognition, including THREADER and GenTHREADER, helped establish core approaches for detecting structural relationships from sequence. He went on to develop FRAGFOLD, an early fragment-assembly approach for de novo tertiary structure prediction, and PSIPRED, one of the most influential and widely used machine-learning-based methods for protein secondary-structure prediction. His later work on amino-acid covariation, including PSICOV and MetaPSICOV, contributed directly to the conceptual and technical advances that made modern deep-learning-based protein modelling possible.

The scale of Professor Jones’s scientific impact is exceptional. His publications have attracted tens of thousands of citations, with an H-index approaching 90 and numerous papers cited more than 1,000 times. His most influential contributions include foundational work on protein fold recognition, secondary-structure prediction, CATH protein structure classification, disorder prediction, machine learning for protein function, and deep learning in protein modelling. Across these areas, his work has helped define how AI and advanced computation can be used not merely to analyse biological data, but to make accurate, testable predictions about biological structure and function. He has been recognised among the world’s most highly cited researchers and ranked among the leading scientists globally in standardised citation metrics.

A hallmark of Professor Jones’s career has been the translation of advanced AI and computational methods into freely available, robust tools used by the international biomedical community. The PSIPRED Protein Analysis Workbench, developed and sustained by his group, has become a landmark community resource, supporting millions of analyses and serving hundreds of users per day. Its recognition as an ELIXIR UK Node Resource reflects both its scientific importance and its enduring value to researchers worldwide. Through resources such as PSIPRED, Professor Jones has ensured that sophisticated predictive methods are not confined to specialist computational groups, but are made available to experimental biologists, biomedical researchers, and the wider life-sciences community.

Professor Jones has also made major contributions to one of the most celebrated scientific advances of recent years: AlphaFold. From 2016 to 2019, he served as a consultant to Google DeepMind, helping guide the AlphaFold project during its formative first 18 months as the team’s sole specialist in bioinformatics and protein structure prediction. This contribution placed him at a critical interface between decades of protein-structure prediction research and the emergence of large-scale AI systems for biology. The project subsequently became one of the defining achievements of AI-enabled biology, and Professor Jones’s involvement reflects the depth of his influence on the field from which that breakthrough emerged.

His international standing is reflected in major honours including election as a Fellow of the Royal Society, Fellow of the International Society for Computational Biology, and Foreign Correspondent Academician of the Academy of Sciences of the Institute of Bologna. He has delivered keynote, plenary, and invited lectures across Europe, North America, Asia, and Australia, and has contributed to major international scientific assessments and advisory panels. These roles reflect not only his distinction as a computational biologist, but also his status as one of the scientists who helped establish AI-driven approaches as central to modern biological research.

Professor Jones has sustained a strong record of research leadership and funding success, including major awards from BBSRC, Wellcome Trust, industry partners, and the European Research Council. His ERC Advanced Grant supported pioneering work on amino-acid covariation and protein modelling, while more recent awards have advanced deep-learning methods, differentiable programming, and the next generation of PSIPRED resources. This funding record demonstrates sustained confidence in his ability to open new directions at the interface of AI, computation, and molecular biology.

Alongside his research, Professor Jones has made substantial contributions to teaching, mentorship, institutional leadership, and external engagement. He has supervised doctoral researchers who have progressed into senior academic, industry, publishing, and biotechnology roles; led bioinformatics teaching at UCL; directed major research and service activities; and contributed to national funding, fellowship, and review panels. His commercial and translational experience includes software licensing, industry collaborations with major pharmaceutical companies, involvement in the UCL spin-out Inpharmatica, and consultancy with leading biotechnology and AI organisations.

Taken together, Professor Jones’s career represents a rare combination of scientific originality, field-building influence, technical delivery, public research infrastructure, international leadership, and demonstrable real-world impact. He has not only contributed to many of the decisive advances in computational protein science, but has also helped lay the foundations for AI as a transformative force in biology. His work shows how rigorous computational science, machine learning, and biological insight can be combined to solve fundamental problems in the life sciences, and he has ensured that those advances are accessible to, and used by, the global biomedical community.