{"id":"drug-discovery-roadmap","name":"Drug discovery roadmap: screening in mice → maps of dependency → designing in silico","route":"/roadmaps/drug-discovery-roadmap/","eras":[{"era":"1950s-1990s","title":"Screening nature and hoping","description":"The NCI screened tens of thousands of compounds in mouse leukaemias and, from 1990, in a panel of sixty human cell lines. It found the periwinkle alkaloid vincristine, the yew-bark taxane paclitaxel and the antibiotic dactinomycin, and it established the pipeline everyone still uses: cells, then mice, then people. What it could not do was say which people.","status":"historic","refs":[{"id":"nci","kind":"institution","name":"National Cancer Institute (NIH)","route":"/institutions/nci/","tldr":"The NCI is the US government's cancer research agency, spending ~$7B a year and running the Cancer Centers Program, TCGA, and Rosenberg's cell therapy lab."},{"id":"vincristine","kind":"drug","name":"Vincristine","route":"/drugs/vincristine/","status":"approved","tldr":"A plant-derived chemotherapy from the Madagascar periwinkle that has been in almost every childhood leukaemia and lymphoma regimen since the 1960s."},{"id":"paclitaxel","kind":"drug","name":"Paclitaxel / nab-paclitaxel","route":"/drugs/paclitaxel/","status":"approved","tldr":"A microtubule poison discovered in the Pacific yew tree, among the most used chemotherapies in breast, lung, and ovarian cancer."},{"id":"dactinomycin","kind":"drug","name":"Dactinomycin (actinomycin D)","route":"/drugs/dactinomycin/","status":"approved","tldr":"The first antibiotic used as an anticancer drug (1954) and still the core of chemotherapy for Wilms tumour, rhabdomyosarcoma and gestational trophoblastic disease."},{"id":"pdx-models","kind":"technology","name":"Patient-derived xenografts","route":"/technologies/pdx-models/","status":"established","tldr":"A patient-derived xenograft is a patient's tumour grown in a mouse, used to test drugs before they reach people."}],"trials":[],"papers":[]},{"era":"1990s-2015","title":"Targets, structures and high-throughput screens","description":"Cloning the oncogenes gave discovery a target; crystal structures gave it a shape to fit; robotic screening of millions of compounds and later DNA-encoded libraries gave it throughput. Imatinib was the proof. The cost was a generation of drugs that hit their target and did nothing for patients, because the cell-line and xenograft models that selected them did not represent human tumours. Nine in ten oncology drugs entering trials still fail.","status":"historic","refs":[{"id":"imatinib","kind":"drug","name":"Imatinib","route":"/drugs/imatinib/","status":"approved","tldr":"The drug that started the targeted therapy era in 2001, turning chronic myeloid leukaemia into a manageable condition with near-normal life expectancy."},{"id":"high-throughput-screening-libraries","kind":"technology","name":"High-throughput screening and DNA-encoded libraries","route":"/technologies/high-throughput-screening-libraries/","status":"established","tldr":"Testing millions or billions of chemical compounds against a cancer target automatically to find starting points for new drugs."},{"id":"structural-biology-infrastructure","kind":"technology","name":"Structural biology infrastructure (cryo-EM, synchrotrons, AlphaFold)","route":"/technologies/structural-biology-infrastructure/","status":"established","tldr":"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."},{"id":"b-preclinical-models","kind":"bottleneck","name":"Lab models that fail to predict what happens in patients","route":"/bottlenecks/b-preclinical-models/","tldr":"Nine in ten cancer drugs that work in mice fail in humans. Our models are the reason."},{"id":"pdx-models","kind":"technology","name":"Patient-derived xenografts","route":"/technologies/pdx-models/","status":"established","tldr":"A patient-derived xenograft is a patient's tumour grown in a mouse, used to test drugs before they reach people."}],"trials":[],"papers":[]},{"era":"2015-2024","title":"Maps of dependency and models closer to the patient","description":"Genome-wide CRISPR knockout screens across a thousand cell lines (DepMap) list which genes each cancer cannot live without, and expose synthetic-lethal pairs such as PRMT5 in MTAP-deleted tumours and WRN in mismatch-repair-deficient ones. Patient-derived organoids keep a tumour's architecture and drug response in a dish; xenograft banks keep it in a mouse; TCGA, GENIE and CPTAC supply the genomes and proteomes to interpret them. The first drugs found this way are now in trials.","status":"current","refs":[{"id":"crispr-screens","kind":"technology","name":"CRISPR functional genomics","route":"/technologies/crispr-screens/","status":"established","tldr":"Knocking out every gene one at a time in cancer cells to find which ones they cannot live without."},{"id":"depmap","kind":"collection","name":"DepMap (Cancer Dependency Map)","route":"/collections/depmap/","tldr":"Which genes each cancer cell line cannot live without. The map of synthetic-lethal targets."},{"id":"synthetic-lethality-approaches","kind":"technology","name":"Synthetic lethality approaches","route":"/technologies/synthetic-lethality-approaches/","status":"approved","tldr":"Finding a second gene that a cancer needs only because its first gene is broken, then hitting the second one."},{"id":"organoids","kind":"technology","name":"Patient-derived organoids","route":"/technologies/organoids/","status":"established","tldr":"Patient-derived organoids are miniature 3D versions of a patient's tumour grown in the lab."},{"id":"hub-organoids","kind":"company","name":"HUB Organoids","route":"/companies/hub-organoids/","tldr":"The Clevers-lab spin-out that licenses patient-derived organoid technology and maintains a living biobank."},{"id":"pdx-models","kind":"technology","name":"Patient-derived xenografts","route":"/technologies/pdx-models/","status":"established","tldr":"A patient-derived xenograft is a patient's tumour grown in a mouse, used to test drugs before they reach people."},{"id":"champions-oncology","kind":"company","name":"Champions Oncology","route":"/companies/champions-oncology/","tldr":"Oncology CRO with the largest bank of patient-derived xenograft models (TumorGraft), now adding 3D organoid screening and radiopharmaceutical services."},{"id":"cancer-models","kind":"collection","name":"Cancer Models (PDCM Finder) & HCMI","route":"/collections/cancer-models/","tldr":"Find a mouse or dish model that matches a tumour type or mutation."},{"id":"tcga-gdc","kind":"collection","name":"TCGA / NCI Genomic Data Commons","route":"/collections/tcga-gdc/","tldr":"The reference atlas of cancer genomes that most cancer biology since 2008 is built on."},{"id":"genie","kind":"collection","name":"AACR Project GENIE","route":"/collections/genie/","tldr":"AACR Project GENIE is real-world tumour sequencing data shared by leading cancer centres."},{"id":"cptac","kind":"collection","name":"CPTAC (Clinical Proteomic Tumor Analysis Consortium)","route":"/collections/cptac/","tldr":"CPTAC measures the proteins, not just the genes, of thousands of tumours."},{"id":"proteomics","kind":"technology","name":"Proteomics & phosphoproteomics","route":"/technologies/proteomics/","status":"emerging","tldr":"Measuring the proteins in a tumour, which is what drugs actually hit, rather than the genes that encode them."}],"trials":[],"papers":[]},{"era":"2020-2026","title":"Structure prediction and generative design","description":"AlphaFold turned protein structure into a lookup, and AlphaFold 3 (2024), Boltz and Chai extended it to drug and antibody complexes; RFdiffusion and ESM3 design proteins from scratch. Insilico's generative chemistry produced the first AI-discovered drug to reach phase 2; Isomorphic's first oncology candidate entered trials; Recursion merged with Exscientia to pair image-based biology with design; Xaira launched with over a billion dollars to build discovery around these models. The honest scorecard: faster hit-to-candidate, no approved cancer drug yet.","status":"current","refs":[{"id":"alphafold3","kind":"technology","name":"AlphaFold 3","route":"/technologies/alphafold3/","status":"established","tldr":"Predicts the 3D shape of proteins together with DNA, RNA, small molecules and antibodies, the starting point for much modern drug design."},{"id":"boltz","kind":"technology","name":"Boltz-1 / Boltz-2 (MIT, open)","route":"/technologies/boltz/","status":"emerging","tldr":"Open-source structure models that match AlphaFold 3, with Boltz-2 also predicting how strongly a drug binds."},{"id":"chai-1","kind":"technology","name":"Chai-1 / Chai-2","route":"/technologies/chai-1/","status":"emerging","tldr":"Structure and antibody-design models from Chai Discovery, with Chai-2 reporting high zero-shot antibody hit rates."},{"id":"chai-discovery","kind":"company","name":"Chai Discovery","route":"/companies/chai-discovery/","tldr":"Makes Chai-1 and Chai-2, open-weight structure models used for antibody and binder design."},{"id":"rfdiffusion","kind":"technology","name":"RFdiffusion / RFdiffusion2 and ProteinMPNN (Baker Lab)","route":"/technologies/rfdiffusion/","status":"emerging","tldr":"The tools that design entirely new proteins to bind a chosen target, now used for cancer binders and antibodies."},{"id":"esm3","kind":"technology","name":"ESM3 (EvolutionaryScale)","route":"/technologies/esm3/","status":"emerging","tldr":"ESM3 is a generative protein model that designed a working fluorescent protein far from any natural sequence."},{"id":"ai-drug-design","kind":"technology","name":"AI-driven drug & target discovery","route":"/technologies/ai-drug-design/","status":"phase-2","tldr":"Using machine learning to pick targets, design molecules and antibodies, and predict which ADC will work."},{"id":"chemistry42","kind":"technology","name":"Chemistry42 and Pharma.AI (Insilico)","route":"/technologies/chemistry42/","status":"emerging","tldr":"Generative chemistry platform behind the first AI-discovered drug to reach phase 2, plus oncology candidates."},{"id":"insilico-medicine","kind":"company","name":"Insilico Medicine","route":"/companies/insilico-medicine/","tldr":"Generative-AI drug discovery company, listed in Hong Kong in December 2025, with a pan-KRAS candidate and a pan-TEAD inhibitor in the clinic."},{"id":"isomorphic-labs","kind":"company","name":"Isomorphic Labs","route":"/companies/isomorphic-labs/","tldr":"Alphabet's AlphaFold-derived drug design company; its first AI-designed oncology candidate was cleared for human trials in January 2026 after a $2.1B raise."},{"id":"google-deepmind","kind":"company","name":"Google DeepMind (and Google Research)","route":"/companies/google-deepmind/","tldr":"Google DeepMind built AlphaFold, AlphaMissense, AlphaGenome and Med-Gemini, the reference models for structure, variants, and medical multimodal reasoning."},{"id":"recursion","kind":"company","name":"Recursion Pharmaceuticals","route":"/companies/recursion/","tldr":"Recursion is an AI-first biotech (merged with Exscientia in 2024) with a clinical oncology pipeline that includes an RBM39 degrader and a MEK inhibitor for familial adenomatous polyposis."},{"id":"exscientia","kind":"company","name":"Exscientia","route":"/companies/exscientia/","tldr":"Exscientia was a British pioneer in designing drugs with artificial intelligence, including cancer drugs that reached early clinical trials. It has been folded into the US company Recursion."},{"id":"phenom-2","kind":"technology","name":"Phenom-2 and Recursion OS","route":"/technologies/phenom-2/","status":"emerging","tldr":"A model trained on billions of cell microscopy images to read what a drug or gene knockout does to a cell."},{"id":"xaira-therapeutics","kind":"company","name":"Xaira Therapeutics","route":"/companies/xaira-therapeutics/","tldr":"Launched in 2024 with over $1 billion to build AI-native drug discovery from Baker-lab protein design."},{"id":"generate-biomedicines","kind":"company","name":"Generate:Biomedicines","route":"/companies/generate-biomedicines/","tldr":"Generative-AI protein design company (Flagship) that went public in 2026 and is testing an antibody that neutralises leaked ADC payload to reduce side effects."}],"trials":[],"papers":[]},{"era":"2015-2026","title":"New modalities as platforms","description":"Discovery is no longer only about small molecules. Degrader and molecular glue platforms remove proteins that cannot be inhibited; ADC linker chemistry (Araris) and bicyclic peptide conjugates (Bicycle) turn a payload into a targeted drug; oligonucleotides silence genes; chemoproteomics finds covalent handles on KRAS. Each platform generates candidates faster than trials can test them, which moves the constraint downstream.","status":"current","refs":[{"id":"protac-degrader","kind":"technology","name":"PROTACs & molecular glues (targeted protein degradation)","route":"/technologies/protac-degrader/","status":"approved","tldr":"Instead of blocking a protein, these drugs tag it for the cell's own garbage disposal, removing it entirely."},{"id":"molecular-glue-platforms","kind":"technology","name":"Molecular glue discovery platforms","route":"/technologies/molecular-glue-platforms/","status":"phase-1","tldr":"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."},{"id":"degrader-antibody-conjugate","kind":"technology","name":"Degrader-antibody conjugate (DAC)","route":"/technologies/degrader-antibody-conjugate/","status":"phase-1","tldr":"An ADC that delivers a protein-destroying molecule instead of chemotherapy, hitting targets inside the cell that were previously unreachable."},{"id":"araris","kind":"company","name":"Araris Biotech (Taiho)","route":"/companies/araris/","tldr":"Swiss linker-technology company (AraLinQ) acquired by Taiho for up to $1.14B; first clinical ADC ARC-02 (CD79b) dosed in June 2026."},{"id":"bicycle-therapeutics","kind":"company","name":"Bicycle Therapeutics","route":"/companies/bicycle-therapeutics/","tldr":"Inventor of bicyclic peptide drug conjugates; its lead Nectin-4 conjugate was deprioritised in 2026 after regulatory feedback."},{"id":"peptide-drug-conjugate","kind":"technology","name":"Peptide-drug & small-molecule-drug conjugates","route":"/technologies/peptide-drug-conjugate/","status":"approved","tldr":"Like an ADC but with a small targeting peptide instead of an antibody, so it penetrates tumours faster and is cheaper to make."},{"id":"antisense-sirna","kind":"technology","name":"Oligonucleotide therapeutics","route":"/technologies/antisense-sirna/","status":"phase-2","tldr":"Oligonucleotide therapeutics are short synthetic strands of genetic code that silence a specific cancer gene."},{"id":"frontier-medicines","kind":"company","name":"Frontier Medicines","route":"/companies/frontier-medicines/","tldr":"Chemoproteomics company with FMC-376, a KRAS G12C inhibitor that hits both the ON and OFF states of the protein, in phase 1/2."},{"id":"sotorasib","kind":"drug","name":"Sotorasib","route":"/drugs/sotorasib/","status":"approved","tldr":"Sotorasib (Lumakras) was the first drug to hit KRAS, approved in 2021 after four decades of failure."},{"id":"adc-generations","kind":"roadmap","name":"ADC roadmap: from Mylotarg to bispecific and dual-payload ADCs","route":"/roadmaps/adc-generations/","tldr":"Twenty-five years of trying to make chemotherapy hit only cancer cells, from the unstable first ADC to today's third-generation blockbusters and the fourth generation now in trials."}],"trials":[],"papers":[]},{"era":"2026-2030","title":"Functional precision medicine","description":"Instead of inferring drug response from genotype, test the drug on the patient's own cells: organoid pharmacotyping in pancreatic cancer, the PARIS organoid screen, tumour fragments kept alive with their vessels, BH3 profiling in leukaemia. The proposal that would make it a field is to grow each trial patient's tumour as organoids and let the results decide which platform arm opens next, with shared reference organoid and xenograft panels so every laboratory tests against the same models.","status":"emerging","refs":[{"id":"functional-drug-testing","kind":"technology","name":"Functional (ex vivo) drug testing","route":"/technologies/functional-drug-testing/","status":"emerging","tldr":"Growing a patient's own cancer cells in a dish and testing drugs on them directly, instead of guessing from genetics."},{"id":"organoid-guided-therapy-scale","kind":"technology","name":"Organoid-guided therapy at scale","route":"/technologies/organoid-guided-therapy-scale/","status":"phase-2","tldr":"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."},{"id":"pdac-organoid-pharmacotyping","kind":"technology","name":"PDAC organoid pharmacotyping","route":"/technologies/pdac-organoid-pharmacotyping/","status":"emerging","tldr":"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."},{"id":"sengine","kind":"company","name":"SEngine Precision Medicine","route":"/companies/sengine/","tldr":"Runs the PARIS test: a patient's tumour grown as organoids and screened against 240+ drugs to find options sequencing cannot see."},{"id":"curesponse","kind":"company","name":"Curesponse","route":"/companies/curesponse/","tldr":"Israeli company whose cResponse test keeps a patient's tumour fragment alive, with its vessels and immune cells, to test which treatments it responds to."},{"id":"bh3-profiling","kind":"technology","name":"BH3 profiling (functional apoptosis testing)","route":"/technologies/bh3-profiling/","status":"emerging","tldr":"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."},{"id":"idea-tr2-organoid-coclinical-arms","kind":"idea","name":"Grow each trial patient's tumour as organoids to decide which platform arm opens next","route":"/ideas/idea-tr2-organoid-coclinical-arms/","tldr":"While patients are treated in a platform trial, their tumour cells grow in a dish and are tested against dozens of drug pairs. The pairs that win in the dish become the next arms."},{"id":"idea-tr2-reference-model-panels","kind":"idea","name":"Shared reference organoid and PDX panels that every lab can test against","route":"/ideas/idea-tr2-reference-model-panels/","tldr":"If every lab had access to the same set of well-characterised tumour models, results could be compared directly instead of each lab using its own private models."}],"trials":[],"papers":[]},{"era":"2026-2030","title":"Perturbation data at the scale the problem needs","description":"Tahoe-100M measured a hundred million single cells across 1,100 drugs and fifty cancer lines; Arc's Virtual Cell Atlas and State model, Geneformer and scGPT are the attempts to learn from that scale how a cell will respond to a perturbation it has never seen. Rigorous benchmarks show current models barely beat simple baselines on unseen contexts, which is the right kind of bad news: the problem is now measurable. The virtual cell roadmap follows this in detail.","status":"emerging","refs":[{"id":"tahoe-100m","kind":"collection","name":"Tahoe-100M","route":"/collections/tahoe-100m/","tldr":"Tahoe-100M is the biggest single-cell dataset ever released, built to teach AI how cancer cells respond to drugs."},{"id":"vevo-therapeutics","kind":"company","name":"Vevo Therapeutics","route":"/companies/vevo-therapeutics/","tldr":"Produced Tahoe-100M, the largest single-cell drug-perturbation atlas, and trains models on it."},{"id":"arc-virtual-cell-atlas","kind":"collection","name":"Arc Virtual Cell Atlas","route":"/collections/arc-virtual-cell-atlas/","tldr":"Arc's growing library of cell data, the fuel for virtual cell models."},{"id":"arc-institute","kind":"institution","name":"Arc Institute","route":"/institutions/arc-institute/","tldr":"The Arc Institute is a well-funded nonprofit research institute building 'virtual cell' AI models and the datasets to train them."},{"id":"state-arc","kind":"technology","name":"State (Arc Institute perturbation model)","route":"/technologies/state-arc/","status":"emerging","tldr":"Predicts how cells will respond to a drug or gene knockout, trained on over 100 million perturbed cells."},{"id":"geneformer","kind":"technology","name":"Geneformer","route":"/technologies/geneformer/","status":"emerging","tldr":"Geneformer is a transformer trained on about 30 million single cells that encodes each cell as a ranked list of its genes, so deleting a gene in silico shows which genes matter in a disease. It was the first single-cell foundation model in general use, though benchmarks find only modest gains over linear baselines on some tasks."},{"id":"scgpt","kind":"technology","name":"scGPT","route":"/technologies/scgpt/","status":"emerging","tldr":"A GPT-style model for single-cell data that predicts cell types, perturbation responses, and gene networks."},{"id":"virtual-cell","kind":"roadmap","name":"Virtual cell roadmap: from bulk omics to a predictive model of a cancer cell","route":"/roadmaps/virtual-cell/","tldr":"The attempt to build a computer model of a cell good enough to predict what a drug or mutation will do before anyone runs the experiment."}],"trials":[],"papers":[]},{"era":"2030+","title":"In silico first","description":"The long-range bet is that a candidate is designed, its dose chosen and its toxicity screened in silico and on linked human organ chips before the first mouse, and that digital twins reduce the size of the trials that follow. That requires models that generalise, which requires data that reproduce. A funded replication in every cancer biology PhD and a registry for preclinical experiments that did not work are the unglamorous prerequisites.","status":"speculative","refs":[{"id":"idea-bio1-in-silico-trials-dose","kind":"idea","name":"In silico trials to choose the dose before the first patient","route":"/ideas/idea-bio1-in-silico-trials-dose/","tldr":"Simulating thousands of virtual patients on a computer can suggest which dose and schedule to test, so fewer real patients receive doses that are too high or too low."},{"id":"idea-bio1-multi-organ-chip-tox","kind":"idea","name":"Linked human organ chips to predict side effects before people are dosed","route":"/ideas/idea-bio1-multi-organ-chip-tox/","tldr":"Damage to the lungs, heart or liver is a common reason cancer drugs fail. Connected chips of human tissue may spot this earlier than animal tests."},{"id":"digital-twins-trials","kind":"technology","name":"Digital twins and virtual control arms","route":"/technologies/digital-twins-trials/","status":"emerging","tldr":"Using a model of what would have happened to a patient on standard treatment, so fewer people have to be randomised to it."},{"id":"de-novo-protein-design","kind":"technology","name":"De novo designed protein binders","route":"/technologies/de-novo-protein-design/","status":"phase-1","tldr":"Designing a protein from scratch on a computer to grip a chosen target, instead of finding one in an animal or a library."},{"id":"idea-tr2-phd-replication-year","kind":"idea","name":"Every cancer biology PhD begins with a funded replication of a published finding","route":"/ideas/idea-tr2-phd-replication-year/","tldr":"Make the first project of every cancer biology doctoral student a funded, pre-registered attempt to repeat a published finding chosen from a curated list of translationally relevant results, with the outcome published in a replication registry. Students learn power analysis, blinding and reporting, and the field gets thousands of replications a year."},{"id":"idea-tr2-preclinical-negative-registry","kind":"idea","name":"A registry for preclinical experiments that did not work","route":"/ideas/idea-tr2-preclinical-negative-registry/","tldr":"Most lab experiments that fail are never written up, so other labs repeat them. A simple, structured registry with a citable record for each failed experiment would stop the waste."},{"id":"ai-oncology-roadmap","kind":"roadmap","name":"AI in oncology roadmap: pattern readers → foundation models → agents in the workflow","route":"/roadmaps/ai-oncology-roadmap/","tldr":"Artificial intelligence in cancer started as software that flagged spots on a mammogram. It now designs molecules, reads slides better than any single pathologist for some tasks, and is beginning to match patients to trials and draft the tumour board summary; the question is which of it will be proven to help."}],"trials":[],"papers":[]},{"era":"What sets the pace","title":"Models, reproducibility and the valley of death","description":"Preclinical models still do not predict people, fewer than half of landmark findings reproduce, and most academic discoveries die before anyone tests them in humans because no one funds the step between. Companies hold compound libraries and negative results that would save others years. A shared compound pool for rare cancer researchers and a guaranteed purchase prize for the first drug against a named hard target are two proposals that attack the incentive problem directly.","status":"current","refs":[{"id":"b-preclinical-models","kind":"bottleneck","name":"Lab models that fail to predict what happens in patients","route":"/bottlenecks/b-preclinical-models/","tldr":"Nine in ten cancer drugs that work in mice fail in humans. Our models are the reason."},{"id":"b-reproducibility","kind":"bottleneck","name":"Preclinical results do not reproduce","route":"/bottlenecks/b-reproducibility/","tldr":"Fewer than half of landmark cancer biology findings reproduce when someone else tries."},{"id":"b-translational-valley","kind":"bottleneck","name":"The valley of death between lab and product","route":"/bottlenecks/b-translational-valley/","tldr":"Most academic discoveries die before anyone tests them in people because nobody funds the middle step."},{"id":"b-ip-collaboration","kind":"bottleneck","name":"Secrecy and intellectual property block collaboration","route":"/bottlenecks/b-ip-collaboration/","tldr":"Companies with complementary drugs rarely test them together, and data that could answer questions stays locked up."},{"id":"b-negative-results","kind":"bottleneck","name":"Failures are hidden","route":"/bottlenecks/b-negative-results/","tldr":"Negative trials, failed drugs and abandoned programmes are rarely published, so the same mistakes are repeated."},{"id":"b-undruggable-targets","kind":"bottleneck","name":"The undruggable drivers","route":"/bottlenecks/b-undruggable-targets/","tldr":"The proteins that drive most cancers, such as MYC, mutant p53 and most RAS variants, still have no good drug."},{"id":"idea-bio2-shared-compound-access-pool","kind":"idea","name":"A shared compound library that rare cancer researchers can actually use","route":"/ideas/idea-bio2-shared-compound-access-pool/","tldr":"Companies hold thousands of well-characterised drugs that could help rare cancers, but each request takes a year of legal negotiation. One standing agreement would unblock it."},{"id":"idea-bio1-undruggable-market-commitment","kind":"idea","name":"A guaranteed purchase prize for the first drug against a named hard target","route":"/ideas/idea-bio1-undruggable-market-commitment/","tldr":"Governments promised in advance to buy vaccines that did not yet exist, and they got made. The same promise could be made for a drug against a target everyone has given up on."}],"trials":[],"papers":[]}],"watch":[]}