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Coming up: Sat 31 Oct · Frontier Paper Club: DeepSeek-R1: reasoning learned through reinforcement learning; Wed 4 Nov · Build Night: Agents for Science: Build a literature agent that cites its sources; Sat 7 Nov · Frontier Paper Club: AI Co-Scientist: multi-agent hypothesis generation; Sat 14 Nov · Frontier Paper Club: Language agents that synthesise scientific knowledge; Sun 15 Nov · GPU 101: From Notebook to Accelerator: Profile a PyTorch training loop on a free GPU; Wed 18 Nov · Build Night: Agents for Science: Score genetic variants with Evo 2

From reading the literature to trusting the result.

Our work follows a discovery from end to end: AI that reasons over the literature, models that explain the biology, and quality systems that make the outcome safe to use in the clinic.

AI for Accelerated Scientific Workflows & Deep Research

Science is bottlenecked less by data than by the time it takes to read, connect, and test it. We build agentic systems that handle the reading and the bookkeeping, so scientists can spend their time on judgment.

Driving question: Can a team of AI agents propose a hypothesis worth a scientist’s afternoon, and show its work?

  1. Multi-agent scientific reasoning

    Specialist agents (retriever, critic, planner) that argue over evidence and expose their reasoning chain instead of returning a single opaque answer.

    agents · critique · provenance

  2. Automated hypothesis generation

    Proposing and ranking candidate hypotheses by novelty, plausibility, and testability against the existing literature.

    rank · novelty × testability

  3. Biomedical knowledge graph mining

    Linking genes, compounds, phenotypes, and papers into graphs where missing edges become research leads.

    graph · link prediction

  4. Laboratory protocol synthesis

    Turning a hypothesis into a step-by-step, reagent-aware protocol that a bench scientist reviews before anything runs.

    protocol · human-in-the-loop

Computational Biology & Molecular Systems

Biology is a systems problem. We model it across scales, from the fold of a single protein to the wiring of a cellular network, and ask what changes when one variant does.

Driving question: Which molecular changes actually matter, and can we predict them before the wet lab does?

  1. Structural bioinformatics

    Reasoning over protein structure, folding, and binding interfaces to explain function from shape.

    structure · fold · binding

  2. Molecular property prediction

    Learned representations of molecules that estimate properties early, before costly synthesis and assays.

    graph models · property regression

  3. Biological network topology

    Mapping interaction networks to find the hubs, modules, and bottlenecks that hold a system together.

    network · centrality · modules

  4. Genomic variant impact analysis

    Estimating how individual genomic variants propagate into changes in protein function and phenotype.

    variant · effect prediction

Translational Quality & Regulated AI

A model that works in a notebook is not yet a model that works in a clinic. This lab applies the discipline of regulated pharmaceutical quality systems, drawing on the methodologies our founding lead used leading the Quality vertical at GSK, to AI that touches patients.

Driving question: What does it take for an AI system to earn trust in a regulated, high-stakes setting?

  1. High-reliability healthcare pipelines

    Data and model pipelines built to fail loudly: monitored, versioned, and checked for drift before outputs reach a clinician.

    reliability · monitoring · drift

  2. Regulated ML quality systems

    Validation plans, audit trails, and change control for machine learning, adapted from pharmaceutical quality practice.

    validation · audit trail · change control

  3. Clinical data validation

    Checking clinical datasets for completeness, consistency, and lineage so models are trained on data that can be defended.

    data quality · lineage

Clinical relevance is reviewed with our Chief Clinical Advisor, Dr. Sasidhar, MD (General Medicine), of Sasidhar Hospitals.

Meet the team

Working on a related problem?

We collaborate with labs, clinicians, and grant partners across all three pillars.

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