Overview
Background
Prof David Ascher is an NHMRC Investigator Leadership Fellow and Deputy Associate Dean (Research Partnerships) in the Faculty of Science at The University of Queensland. He also serves as Deputy Director of the Australian Centre for Ecogenomics and Head of Computational Biology & Clinical Informatics at the Baker Heart and Diabetes Institute. Internationally, he sits on scientific advisory boards for A*STAR (Singapore), Fiocruz (Brazil) and the Tuscany University Network (Italy), and has been recognised with major honours including the Royal Society of Chemistry Horizon Prize.
A global leader in computational biology and personalised medicine, Prof Ascher develops advanced AI- and structure-based approaches to understand how genetic variation alters protein structure, function, and clinical outcomes. His group has built one of the world’s most widely used platforms for interpreting coding variants—over 90 computational tools, accessed more than 9.5 million times per year from 120+ countries. These tools underpin clinical diagnostics, guide drug development pipelines, and support international public-health responses to antimicrobial resistance and emerging infectious diseases.
His research has led to new molecular insights across infectious disease, rare disease, oncology and cardiometabolic health, and has been translated directly into practice—informing WHO policy, enabling early resistance detection in tuberculosis and leprosy, stratifying patients with hereditary cancers, and supporting vaccine design with partners including Pfizer. Many of his methods are embedded in globally used resources such as Ensembl VEP, PDBe, and the EMBL-EBI KnowledgeBase.
Prof Ascher has a longstanding commitment to interdisciplinary leadership and capability-building across UQ. As Director (Strategy) of the Biotechnology Programs and later as Deputy Associate Dean (Research Partnerships), he has driven initiatives to transform UQ’s biotechnology education, grow industry-embedded training, expand international partnerships, and diversify research income. He has led the development of UQ’s biotechnology–industry placement ecosystem, initiated new professional development programs adopted across multiple Faculties and Institutes, and established major collaborations with government, industry and global research organisations.
He has published more than 250 peer-reviewed papers (over half as senior author: FWCI 2.7), secured more than $30M in competitive research funding, and supervised over 60 HDR students who now hold leadership positions in academia, industry and government. His work appears in leading journals including Nature, Nature Genetics, Science, PNAS and Nature Microbiology, and is cited in over 100 policy documents and 40 patents.
Prof Ascher holds degrees in Biotechnology, Biochemistry, Structural Biology and Law. His research career has spanned Adelaide, Melbourne, Cambridge and Brisbane. After his PhD with Professor Michael Parker, he worked with Sir Tom Blundell at the University of Cambridge, where he led programs in structure-guided drug discovery and protein–protein interaction targeting. He established his independent laboratory at Cambridge and then at the University of Melbourne/Bio21 Institute, before moving to the Baker Institute in 2019 and joining UQ in 2021.
Availability
- Professor David Ascher is:
- Available for supervision
- Media expert
Fields of research
Research impacts
My research develops artificial intelligence and protein-structure tools that help clinicians, public-health agencies and industry understand how genetic changes affect disease, drug resistance and treatment outcomes. These tools are now used globally to guide healthcare decisions, improve patient outcomes and accelerate the development of safer, more effective medicines.
Clinical and Public Health Impact
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Faster detection of antimicrobial resistance: My methods enable early identification of drug-resistant tuberculosis and leprosy directly from genome sequencing, improving treatment decisions for patients in Australia, Europe, Brazil and high-burden regions worldwide.
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Improved diagnosis and counselling for rare disease and cancer: Structural analyses developed by my group are used by clinical teams at Addenbrooke’s Hospital (UK) and the Brazilian Ministry of Health to determine the molecular cause of disease and support personalised patient management.
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Enhanced clinical trial design and patient stratification: Our tools have guided stratification in trials for Mendelian disorders and cardiometabolic disease, ensuring patients receive the most appropriate therapies.
Industry and Economic Impact
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Accelerating drug and biologics development: Our platform is embedded in discovery pipelines across biotechnology and pharmaceutical companies, including Pfizer, GSK, Astex and others, supporting the design of next-generation vaccines, antibiotics and therapeutics.
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Predicting drug toxicity and improving safety: pkCSM—one of our AI platforms—has been licensed internationally and is used to reduce costly late-stage failures in drug development.
Policy and Global Health Impact
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Our work has shaped World Health Organization guidance on genomic surveillance and pandemic preparedness and is cited in over 100 policy documents, helping global agencies make evidence-based decisions about vaccines, emerging variants and antimicrobial resistance.
Open Knowledge and Research Infrastructure
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We develop freely accessible tools used by researchers in more than 120 countries (>9.5 million uses per year).
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Our methods are integrated into major international resources, including:
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PDBe (European Protein Data Bank) for analysing molecular interactions
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Ensembl’s Variant Effect Predictor (VEP) for interpreting human genetic variation
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LSHTM’s TB-Profiler for global TB resistance surveillance
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These resources support thousands of laboratories, from academic groups to diagnostic services and health agencies.
Works
Search Professor David Ascher’s works on UQ eSpace
2025
Journal Article
Haploinsufficient variants in SMAD5 are associated with isolated congenital heart disease
Alankarage, Dimuthu, Leshchynska, Iryna, Portelli, Stephanie, Sipka, Alena, Blue, Gillian M., O'Reilly, Victoria, Das, Debjani, Rath, Emma M., Enriquez, Annabelle, Troup, Michael, Fine, Miriam, Poplawski, Nicola, Verlee, Maxim, Humphreys, David T., Harvey, Richard P., Chapman, Gavin, Kirk, Edwin P., Winlaw, David S., Callewaert, Bert, Chung, Wendy K., Ascher, David, Giannoulatou, Eleni and Dunwoodie, Sally L. (2025). Haploinsufficient variants in SMAD5 are associated with isolated congenital heart disease. Human Genetics and Genomics Advances, 6 (4) 100478, 100478. doi: 10.1016/j.xhgg.2025.100478
2025
Journal Article
Assessing the predicted impact of single amino acid substitutions in MAPK proteins for CAGI6 challenges
Turina, Paola, Petrosino, Maria, Enriquez Sandoval, Carlos A., Novak, Leonore, Pasquo, Alessandra, Alexov, Emil, Alladin, Muttaqi Ahmad, Ascher, David B., Babbi, Giulia, Bakolitsa, Constantina, Casadio, Rita, Cheng, Jianlin, Fariselli, Piero, Folkman, Lukas, Kamandula, Akash, Katsonis, Panagiotis, Li, Minghui, Li, Dong, Lichtarge, Olivier, Mahmud, Sajid, Martelli, Pier Luigi, Pal, Debnath, Panday, Shailesh Kumar, Pires, Douglas E. V., Portelli, Stephanie, Pucci, Fabrizio, Rodrigues, Carlos H. M., Rooman, Marianne, Savojardo, Castrense ... Capriotti, Emidio (2025). Assessing the predicted impact of single amino acid substitutions in MAPK proteins for CAGI6 challenges. Human Genetics, 144 (2-3) 245403, 265-280. doi: 10.1007/s00439-024-02724-8
2025
Journal Article
Proximity proteomics reveals a mechanism of fatty acid transfer at lipid droplet-mitochondria- endoplasmic reticulum contact sites
Bezawork-Geleta, Ayenachew, Devereux, Camille J., Keenan, Stacey N., Lou, Jieqiong, Cho, Ellie, Nie, Shuai, De Souza, David P., Narayana, Vinod K., Siddall, Nicole A., Rodrigues, Carlos H. M., Portelli, Stephanie, Zheng, Tenghao, Nim, Hieu T., Ramialison, Mirana, Hime, Gary R., Dodd, Garron T., Hinde, Elizabeth, Ascher, David B., Stroud, David A. and Watt, Matthew J. (2025). Proximity proteomics reveals a mechanism of fatty acid transfer at lipid droplet-mitochondria- endoplasmic reticulum contact sites. Nature Communications, 16 (1) 2135, 2135. doi: 10.1038/s41467-025-57405-5
2025
Journal Article
Drug resistance-associated mutations in Plasmodium UBP-1 disrupt its essential deubiquitinating activity
Smith, Cameron J., Eavis, Heledd, Briggs, Carla, Henrici, Ryan, Karpiyevich, Maryia, Ansbro, Megan R., Hoshizaki, Johanna, van der Heden van Noort, Gerbrand J., Ascher, David B., Sutherland, Colin J., Lee, Marcus C.S. and Artavanis-Tsakonas, Katerina (2025). Drug resistance-associated mutations in Plasmodium UBP-1 disrupt its essential deubiquitinating activity. Journal of Biological Chemistry, 301 (3) 108266, 108266. doi: 10.1016/j.jbc.2025.108266
2025
Journal Article
Definition and validation of prognostic phenotypes in moderate aortic stenosis
Sen, Jonathan, Wahi, Sudhir, Vollbon, William, Prior, Marcus, de Sá, Alex G.C., Ascher, David B., Huynh, Quan and Marwick, Thomas H. (2025). Definition and validation of prognostic phenotypes in moderate aortic stenosis. JACC: Cardiovascular Imaging, 18 (2), 133-149. doi: 10.1016/j.jcmg.2024.06.013
2025
Journal Article
Exploring the effects of missense mutations on protein thermodynamics through structure-based approaches: findings from the CAGI6 challenges
Rodrigues, Carlos H. M., Portelli, Stephanie and Ascher, David B. (2025). Exploring the effects of missense mutations on protein thermodynamics through structure-based approaches: findings from the CAGI6 challenges. Human Genetics, 144 (2-3) e0217169, 327-335. doi: 10.1007/s00439-023-02623-4
2025
Journal Article
Assessing the predicted impact of single amino acid substitutions in calmodulin for CAGI6 challenges
Turina, Paola, Dal Cortivo, Giuditta, Enriquez Sandoval, Carlos A., Alexov, Emil, Ascher, David B., Babbi, Giulia, Bakolitsa, Constantina, Casadio, Rita, Fariselli, Piero, Folkman, Lukas, Kamandula, Akash, Katsonis, Panagiotis, Li, Dong, Lichtarge, Olivier, Martelli, Pier Luigi, Panday, Shailesh Kumar, Pires, Douglas E. V., Portelli, Stephanie, Pucci, Fabrizio, Rodrigues, Carlos H. M., Rooman, Marianne, Savojardo, Castrense, Schwersensky, Martin, Shen, Yang, Strokach, Alexey V., Sun, Yuanfei, Woo, Junwoo, Radivojac, Predrag, Brenner, Steven E. ... Capriotti, Emidio (2025). Assessing the predicted impact of single amino acid substitutions in calmodulin for CAGI6 challenges. Human Genetics, 144 (2-3) 620793, 113-125. doi: 10.1007/s00439-024-02720-y
2024
Journal Article
Use of the energy waveform electrocardiogram to detect subclinical left ventricular dysfunction in patients with type 2 diabetes mellitus
Soh, Cheng Hwee, de Sá, Alex G. C., Potter, Elizabeth, Halabi, Amera, Ascher, David B. and Marwick, Thomas H. (2024). Use of the energy waveform electrocardiogram to detect subclinical left ventricular dysfunction in patients with type 2 diabetes mellitus. Cardiovascular Diabetology, 23 (1) 91, 91. doi: 10.1186/s12933-024-02141-1
2024
Journal Article
Insights into the structure of NLR family member X1: Paving the way for innovative drug discovery
Jewell, Shannon, Nguyen, Thanh Binh, Ascher, David B. and Robertson, Avril A.B. (2024). Insights into the structure of NLR family member X1: Paving the way for innovative drug discovery. Computational and Structural Biotechnology Journal, 23, 3506-3513. doi: 10.1016/j.csbj.2024.09.013
2024
Journal Article
piscesCSM: prediction of anticancer synergistic drug combinations
AlJarf, Raghad, Rodrigues, Carlos H. M., Myung, Yoochan, Pires, Douglas E. V. and Ascher, David B. (2024). piscesCSM: prediction of anticancer synergistic drug combinations. Journal of Cheminformatics, 16 (1) 81, 81. doi: 10.1186/s13321-024-00859-4
2024
Conference Publication
Identification and functional characterisation of ACTN1 variants in individuals with a distinct clinical subtype of frontonasal dysplasia without platelet disorder
Tan, Tiong Yang, Houweling, Peter, Huang, Shengping, Nguyen, Thanh-Binh, Yap, Patrick, van Dooren, Marieke, Mathijssen, Irene, Delatycki, Martin, Clucas, Luisa, North, Kathryn, Ascher, David, Bell, Katrina and Cox, Timothy C. (2024). Identification and functional characterisation of ACTN1 variants in individuals with a distinct clinical subtype of frontonasal dysplasia without platelet disorder. 57th European Society of Human Genetics (ESHG) Conference, Berlin, Germany, 1-4 June 2024. London, United Kingdom: Nature Publishing Group.
2024
Journal Article
Discriminating Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and comorbid conditions using metabolomics in UK biobank
Huang, Katherine, de Sá, Alex G. C., Thomas, Natalie, Phair, Robert D., Gooley, Paul R., Ascher, David B. and Armstrong, Christopher W. (2024). Discriminating Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and comorbid conditions using metabolomics in UK biobank. Communications Medicine, 4 (1) 248, 1-14. doi: 10.1038/s43856-024-00669-7
2024
Conference Publication
Delineating the phenotypic spectrum of NSF-related disorders
Arkush, L., Coleman, J., Nguyen, T. B., Mulhern, S., Scott, D. Armstrong, Kaliakatsos, M., Lofquist, S., Lieffering, N., Ascher, D., Ben Zeev, B., Hildebrand, M., Scheffer, I. E., Sadleir, L., Gordon, S., Stephenson, S. E., McTague, A. and Howell, K. B. (2024). Delineating the phenotypic spectrum of NSF-related disorders. 15th European Epilepsy Congress, Rome, Italy, 7-11 September 2024. Hoboken, NJ, United States: Wiley-Blackwell Publishing. doi: 10.1111/epi.18151
2024
Journal Article
Rifaximin prophylaxis causes resistance to the last-resort antibiotic daptomycin
Turner, Adrianna M., Li, Lucy, Monk, Ian R., Lee, Jean Y. H., Ingle, Danielle J., Portelli, Stephanie, Sherry, Norelle L., Isles, Nicole, Seemann, Torsten, Sharkey, Liam K., Walsh, Calum J., Reid, Gavin E., Nie, Shuai, Eijkelkamp, Bart A., Holmes, Natasha E., Collis, Brennan, Vogrin, Sara, Hiergeist, Andreas, Weber, Daniela, Gessner, Andre, Holler, Ernst, Ascher, David B., Duchene, Sebastian, Scott, Nichollas E., Stinear, Timothy P., Kwong, Jason C., Gorrie, Claire L., Howden, Benjamin P. and Carter, Glen P. (2024). Rifaximin prophylaxis causes resistance to the last-resort antibiotic daptomycin. Nature, 635 (8040), 969-977. doi: 10.1038/s41586-024-08095-4
2024
Journal Article
AlzDiscovery: A computational tool to identify Alzheimer's disease‐causing missense mutations using protein structure information
Pan, Qisheng, Parra, Georgina Becerra, Myung, Yoochan, Portelli, Stephanie, Nguyen, Thanh Binh and Ascher, David B. (2024). AlzDiscovery: A computational tool to identify Alzheimer's disease‐causing missense mutations using protein structure information. Protein Science, 33 (10) e5147, e5147. doi: 10.1002/pro.5147
2024
Journal Article
A new method for network bioinformatics identifies novel drug targets for mucinous ovarian carcinoma
Craig, Olivia, Lee, Samuel, Pilcher, Courtney, Saoud, Rita, Abdirahman, Suad, Salazar, Carolina, Williams, Nathan, Ascher, David B., Vary, Robert, Luu, Jennii, Cowley, Karla J., Ramm, Susanne, Li, Mark Xiang, Thio, Niko, Li, Jason, Semple, Tim, Simpson, Kaylene J., Gorringe, Kylie L. and Holien, Jessica K. (2024). A new method for network bioinformatics identifies novel drug targets for mucinous ovarian carcinoma. NAR Genomics and Bioinformatics, 6 (3) lqae096, lqae096. doi: 10.1093/nargab/lqae096
2024
Journal Article
EFG‐CS: Predicting chemical shifts from amino acid sequences with protein structure prediction using machine learning and deep learning models
Gu, Xiaotong, Myung, Yoochan, Rodrigues, Carlos H. M. and Ascher, David B. (2024). EFG‐CS: Predicting chemical shifts from amino acid sequences with protein structure prediction using machine learning and deep learning models. Protein Science, 33 (8) e5096, e5096. doi: 10.1002/pro.5096
2024
Journal Article
MTR3D‐AF2: Expanding the coverage of spatially derived missense tolerance scores across the human proteome using AlphaFold2
Kovacs, Aaron S., Portelli, Stephanie, Silk, Michael, Rodrigues, Carlos H. M. and Ascher, David B. (2024). MTR3D‐AF2: Expanding the coverage of spatially derived missense tolerance scores across the human proteome using AlphaFold2. Protein Science, 33 (8) e5112, e5112. doi: 10.1002/pro.5112
2024
Conference Publication
Towards evolutionary-based automated machine learning for small molecule pharmacokinetic prediction
de Sá, Alex G. C. and Ascher, David B. (2024). Towards evolutionary-based automated machine learning for small molecule pharmacokinetic prediction. GECCO '24 Companion, Melbourne, VIC, Australia, 14-18 July 2024. New York, NY, United States: ACM. doi: 10.1145/3638530.3664166
2024
Journal Article
Deep-PK: deep learning for small molecule pharmacokinetic and toxicity prediction
Myung, Yoochan, de Sá, Alex G. C. and Ascher, David B. (2024). Deep-PK: deep learning for small molecule pharmacokinetic and toxicity prediction. Nucleic Acids Research, 52 (W1), W469-W475. doi: 10.1093/nar/gkae254
Funding
Current funding
Past funding
Supervision
Availability
- Professor David Ascher is:
- Available for supervision
Looking for a supervisor? Read our advice on how to choose a supervisor.
Available projects
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Treating the Person, Not the Disease: AI-Driven Precision Medicine
Patients with the same diagnosis can have dramatically different outcomes, driven by subtle—but crucial—genomic differences. This project develops state-of-the-art computational approaches to map how individual genetic variation reshapes molecular pathways, protein structure/function and cellular networks.
What the student will investigate
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AI and structural modelling approaches to predict the molecular consequences of patient variants
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Network-level analysis of how mutations rewire biological systems
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Linking molecular perturbations to patient outcomes, drug response and disease trajectories
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Identifying personalised therapeutic opportunities and biomarkers
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Understanding heterogeneity in large clinical and multi-omics datasets
Impact The project will create tools that support personalised treatment strategies, guide clinical decision-making and identify disease mechanisms that cannot be detected by sequence analysis alone.
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Discovering Sequence–Structure–Dynamics–Function Relationships at Scale
Understanding how proteins work requires more than static structures: biological function emerges from the interplay between sequence, 3D structure, conformational dynamics, and molecular interactions. This project uncovers these relationships using modern structural biology, physics-informed AI and large-scale protein datasets.
What the student will explore
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Integrating AlphaFold2/ESMFold models with experimental structure and dynamics data (MD, cryo-EM, NMR)
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Using AI to infer dynamic landscapes and allosteric networks
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Mapping how sequence variation alters conformation and function across protein families
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Predicting structure–dynamics–function relationships at proteome scale
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Developing interpretable models to reveal fundamental design rules of proteins
Impact Findings will drive new biological insights, facilitate annotation of uncharacterised proteins, and support rational engineering of molecules for biotechnology and medicine.
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Developing Better and Safer Drugs through Computational Design
Drug development remains slow and costly, with most failures occurring because candidate molecules lack efficacy or show unacceptable toxicity. This project uses AI-driven modelling and molecular simulation to design safer drugs earlier, and to anticipate resistance before it appears in the clinic.
Student research opportunities
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Predicting and optimising binding affinity using graph-based and deep learning models
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Modelling pharmacokinetics, solubility and toxicity using next-generation multi-task AI
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Using protein structure to identify vulnerable hotspots for resistance mutations
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Designing “resistance-resistant” therapeutics through structure-guided strategies
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Integrating experimental, structural and real-world data to improve predictive performance
Impact This work reduces downstream clinical trial failures, supports pharmaceutical partners, and accelerates the development of new therapeutics for infectious disease, cancer and complex conditions.
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Engineering Biotherapeutics Using Evolution, Structure and AI
Evolution provides powerful blueprints for engineering new proteins and biologics. By integrating ancestral sequence reconstruction, structural modelling, laboratory evolution and AI-driven design, this project aims to create next-generation biotherapeutics with tailored stability, specificity and function.
Student directions may include
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Inferring evolutionary constraints to guide protein engineering
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Designing highly stable or ultra-specific enzymes using structural insights
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Developing biologics and peptides for applications such as toxin neutralisation, enzyme replacement or immune modulation
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Using generative AI to create novel protein scaffolds with enhanced activity
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Reconstructing and evolving ancestral proteins to reveal mechanisms that can be exploited for engineering
Impact The project enables rational design of powerful new enzymes and biologics, supporting applications in medicine, biodefence, sustainable biotech and advanced therapeutics.
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Quantum-Accelerated AI for Protein Dynamics and Variant Prediction
Quantum computing is rapidly advancing, offering fundamentally new ways to represent and manipulate high-dimensional biological systems. This project will develop hybrid quantum-classical machine learning models to simulate protein conformational changes, binding interactions and mutation effects at unprecedented resolution.
Research avenues:
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Quantum kernels for molecular graphs and structural signatures
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Variational quantum circuits for modelling protein flexibility
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Quantum generative models for predicting mutation impacts
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Benchmarking speed/accuracy trade-offs on real quantum hardware
Impact: Creates a next-generation platform for structural biology and personalised variant interpretation—beyond what is feasible with classical ML.
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Foundation Models for Biology: Building the “Biology GPT”
Large language models trained on protein sequences, structures, cellular assays and phenotypic data offer a unified way to predict biological behaviour. This PhD will develop multimodal biological foundation models that integrate sequences, structural embeddings (e.g., AlphaFold2), small-molecule chemistry and clinical genomics.
Research avenues:
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Joint embedding of sequence + structure + function datasets
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Continual learning from experimental perturbation data
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Zero-shot prediction of variant consequences
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Interpretable biological attention maps
Impact: A general-purpose model that can reason across molecular levels—helping clinicians and researchers predict what mutations do, even when no experimental data exist.
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Generative Design of Protein–Protein Interaction Modulators
While many tools predict variant effects, far fewer generate new therapeutic hypotheses. This project will apply state-of-the-art generative AI (diffusion models, graph generative networks) to design peptides, small molecules or molecular glues that modulate protein–protein interactions.
Research avenues:
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Generative design conditioned on structural perturbation signatures
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Modelling mutation-induced rewiring of interaction networks
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Automated in silico screens for stabilisers/destabilisers
Impact: A transformative approach enabling rapid in silico discovery of next-generation precision medicines.
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AI for Evolutionary Forecasting of Pathogens and Antimicrobial Resistance
Predicting future resistance mutations or viral variants is a frontier challenge. This PhD develops deep models that combine structural constraints, evolutionary trajectories and population-level genomics to forecast high-risk mutations before they appear.
Research avenues:
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Deep evolutionary diffusion models
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Graph neural networks integrating protein structure + phylogeny
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Simulation of adaptive landscapes under drug selection pressure
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Real-time forecasting from genomic surveillance data
Impact: Supports global pathogen surveillance, AMR management, and vaccine/drug development.
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Digital Twins for Precision Medicine: AI Models of Individual Patients
This project builds computational digital twins—AI models representing individual patients—by integrating their genomic, structural, biochemical and clinical data.
Research avenues:
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Modelling genotype → molecular phenotype → clinical outcome
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Patient-specific simulation of drug responses
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Identifying compensatory pathways and personalised treatment options
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Multiscale modelling using structural signatures + clinical data
Impact: Provides personalised treatment predictions, informing clinical decision-making and mechanistic understanding of disease.
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Next-Generation Biophysical Simulation using Neural Differential Equations
Traditional molecular simulations remain slow and limited in scale. This project develops AI-accelerated biophysical simulators that learn energy landscapes, dynamics and interaction networks directly from experimental and structural data.
Research avenues:
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Neural ODEs and neural Hamiltonian systems for protein motion
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Predicting long-timescale dynamics from short MD trajectories
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AI inference of allosteric networks and conformational shifts
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Integration with cryo-EM, NMR and AlphaFold2 dynamics models
Impact: Enables accurate simulation of biological processes previously inaccessible due to computational cost.
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Supervision history
Current supervision
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Doctor Philosophy
Leveraging large-scale population data to improve missense variant pathogenicity prediction
Principal Advisor
Other advisors: Dr Stephanie Portelli
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Doctor Philosophy
Using Deep Learning in Cell & Gene Therapy
Principal Advisor
Other advisors: Dr Stephanie Portelli
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Doctor Philosophy
Structure-Guided Deep Learning Approaches for Protein Thermodynamics: Predicting Variant Effects on Protein Stability and Interactions
Principal Advisor
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Doctor Philosophy
Deep Learning Algorithms for Polygenic Genotype-Phenotype Predictions and the development of genetics computation tools
Principal Advisor
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Doctor Philosophy
Using AI to explore phosphorylation in disease
Principal Advisor
Other advisors: Dr Stephanie Portelli
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Doctor Philosophy
Rational protein engineering and inhibition
Principal Advisor
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Doctor Philosophy
Using AI to guide antibody and vaccine design
Principal Advisor
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Doctor Philosophy
Protein structure guided precision medicine
Principal Advisor
Other advisors: Professor Phil Hugenholtz, Dr Stephanie Portelli
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Doctor Philosophy
Improving rational antibody design using machine learning
Principal Advisor
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Doctor Philosophy
Machine Learning for Protein Dynamics: Predicting Post-Translational Modifications and Mutation Effects
Principal Advisor
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Doctor Philosophy
Harnessing AlphaFold and explainable AI to better characterise human missense variants and diseases
Principal Advisor
Other advisors: Dr Stephanie Portelli, Dr Thanh-Binh Nguyen
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Doctor Philosophy
Therapeutic Resolution of Inflammation in the Central Nervous System for Neuroprotection in Parkinson's Disease
Associate Advisor
Other advisors: Professor Avril Robertson
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Doctor Philosophy
Breaking the chain of inflammation through targetting NLR proteins
Associate Advisor
Other advisors: Professor Kate Schroder, Professor Avril Robertson
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Doctor Philosophy
Computational design of targeted lipid technologies
Associate Advisor
Other advisors: Professor Megan O'Mara
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Doctor Philosophy
Unravelling the Physicochemical Drivers of Biomolecular Self-Assembly though Multiscale Simulations
Associate Advisor
Other advisors: Dr Evelyne Deplazes, Professor Megan O'Mara
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Doctor Philosophy
Use of structural phylogeny and reconciliation in molecular phylogenetics
Associate Advisor
Other advisors: Dr Kate Bowerman, Professor Phil Hugenholtz
Completed supervision
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2025
Doctor Philosophy
Post-transcriptional gene regulation: towards a better understanding of pathogenesis and medical applications
Principal Advisor
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2025
Doctor Philosophy
Computational approaches to engineer and modulate G protein-coupled receptors
Principal Advisor
Media
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