Drug Discovery Platforms

AI compute platforms are the GPUs, model libraries, and cloud services that pathology, radiology, and drug-design AI run on.

Using machine learning to pick targets, design molecules and antibodies, and predict which ADC will work.

Predicts the 3D shape of proteins together with DNA, RNA, small molecules and antibodies, the starting point for much modern drug design.

Using viruses that infect bacteria, not human cells, as programmable delivery shells for cancer drugs and vaccines.

A lab test that measures how close a leukaemia cell is to self-destructing, and which survival protein is holding it back, to predict response to venetoclax-type drugs.

Freezers full of consented tumour samples with matched clinical data, which every biomarker and drug programme depends on.

Predicts the many shapes a protein moves between, not just one, thousands of times faster than simulation.

Open-source structure models that match AlphaFold 3, with Boltz-2 also predicting how strongly a drug binds.

Structure and antibody-design models from Chai Discovery, with Chai-2 reporting high zero-shot antibody hit rates.

Generative chemistry platform behind the first AI-discovered drug to reach phase 2, plus oncology candidates.

Knocking out every gene one at a time in cancer cells to find which ones they cannot live without.

Designing a protein from scratch on a computer to grip a chosen target, instead of finding one in an animal or a library.

Using a model of what would have happened to a patient on standard treatment, so fewer people have to be randomised to it.

Folded DNA machines that open only when they touch a tumour, releasing a payload or clotting the tumour's blood supply.

Bacteria that seek out the low-oxygen core of tumours, then manufacture a drug on the spot.

Loading the tiny vesicles cells naturally use to talk to each other with a cancer drug, so the body treats the carrier as its own.

ESM3 is a generative protein model that designed a working fluorescent protein far from any natural sequence.

Growing a patient's own cancer cells in a dish and testing drugs on them directly, instead of guessing from genetics.

The first widely used transformer trained on millions of single cells, able to predict which genes matter in a disease.

Cloud systems where hospitals and researchers store and analyse genomic data securely at petabyte scale.

Testing millions or billions of chemical compounds against a cancer target automatically to find starting points for new drugs.

In vivo base and prime editing would rewrite a cancer's DNA letter by letter inside the body. It works in the liver for inherited disease; nobody has yet corrected a cancer this way in a person.

Reading DNA in very long stretches, which reveals rearrangements and methylation that short-read machines miss.

Molecular glues are small molecules that stick two proteins together so the cell destroys one of them. They are smaller and more drug-like than bifunctional degraders.

Building a trial around one patient, or letting one trial swap drugs in and out as evidence accumulates.

BioNeMo is the software stack many biology foundation models are trained and served with.

Organoid-guided therapy means routinely growing a piece of each patient's tumour and testing drugs on it before choosing, rather than relying on genetics alone.

Patient-derived organoids are miniature 3D versions of a patient's tumour grown in the lab.

A patient-derived xenograft is a patient's tumour grown in a mouse, used to test drugs before they reach people.

Making the microgram-potent toxins inside ADCs, in facilities built so a speck of dust cannot harm a worker.

Growing a patient's pancreatic tumour as mini-organs in a dish and testing chemotherapies on them to pick the regimen most likely to work.

A model trained on billions of cell microscopy images to read what a drug or gene knockout does to a cell.

Programmable DNA-targeting therapeutics are an experimental idea: a drug that reads a cell's DNA, recognises a cancer-specific sequence, and kills only cells that carry it. Change the guide, and the same drug becomes a new drug.

Measuring the proteins in a tumour, which is what drugs actually hit, rather than the genes that encode them.

Machines that measure thousands of proteins at once from tissue or blood, used to find drug targets and early-detection markers.

The tools that design entirely new proteins to bind a chosen target, now used for cancer binders and antibodies.

A GPT-style model for single-cell data that predicts cell types, perturbation responses, and gene networks.

RNA drugs that copy themselves inside the cell, or are made as a loop so they last longer. Both aim to get more protein from a smaller dose.

Reading the genes of each individual cell, and mapping where each cell sits in the tumour.

Site-specific conjugation and linker chemistry decide exactly where and how many payloads attach to the antibody, which determines how safe and effective an ADC is.

Spatial biology instruments are machines that map which genes and proteins are active in each part of a tumour slice.

Predicts how cells will respond to a drug or gene knockout, trained on over 100 million perturbed cells.

Structural biology infrastructure is the microscopes, X-ray sources, and prediction models that show what a cancer protein looks like so chemists can design a drug to fit it.

Finding a second gene that a cancer needs only because its first gene is broken, then hitting the second one.

Testing cheap old drugs, aspirin, metformin, statins, beta-blockers, as cancer treatments, because they are safe, available and sometimes work.

Some tumours contain bacteria and fungi that shelter cancer cells and break down chemotherapy. Killing them may make treatment work.

Stiff, high-pressure tumours squeeze their own blood vessels shut, keeping drugs out. Softening them is a way in.

Producing the engineered viruses that carry a CAR gene into T cells. Viral vector manufacturing is a long-standing bottleneck for cell and gene therapy.

Reading all the genes (exome) or the entire DNA (genome) of a tumour, rather than a chosen panel.