Life sciences
AI applied to living systems: molecular biology, genetics, neuroscience and the study of organisms.
17 milestones
AlphaFold 3 predicts how proteins interact with DNA, RNA and drugs
Google DeepMind and Isomorphic Labs published AlphaFold 3 in Nature on 8 May 2024. Where AlphaFold 2 predicted the shape of a protein, this predicts the joint structure of complexes containing proteins, nucleic acids, small molecules and ions, using a diffusion architecture that produces atom coordinates directly.
AlphaMissense scores every possible missense variant in the human proteome
Google DeepMind published AlphaMissense in Science on 19 September 2023, an adaptation of AlphaFold that scores how likely a single amino acid change is to cause disease. It classified 89 per cent of all 71 million possible missense variants, against roughly 0.1 per cent confirmed by laboratory or clinical work.
RFdiffusion designs proteins that do not exist in nature
Watson, Juergens and colleagues at the Baker Lab published RFdiffusion in Nature on 11 July 2023. By fine-tuning the RoseTTAFold prediction network on denoising tasks, they turned a model that reads protein structures into one that invents them. Hundreds of the designs were then made in the laboratory.
ESMFold predicts structure without alignments and folds 617 million proteins
Lin and colleagues at Meta published ESMFold in Science on 16 March 2023. It predicts a protein's structure from its sequence alone, with no search for related sequences, because the evolutionary information such a search supplies is already inside a language model of 15 billion parameters. They used that speed to fold over 617 million metagenomic proteins.
RoseTTAFold reproduces protein structure prediction and releases the code
Baek and colleagues at the Institute for Protein Design published RoseTTAFold in Science on 15 July 2021, having rebuilt protein structure prediction from the ideas DeepMind had described at CASP14 eight months earlier without releasing. Its accuracy approached AlphaFold 2 without matching it, and it ran in about ten minutes on a gaming computer.
Reinforcement learning framework proposed to model T-cell adaptive immune response
In 2021, researchers published in Physical Review Research a theoretical framework proposing that T-cell receptor signalling during adaptive immunity can be formally described as a reinforcement learning process, connecting immunological learning to established machine-learning theory.
DeepMind's AlphaFold 2 Achieves Highest-Accuracy Results at CASP14 Protein Structure Prediction Competition
In November–December 2020, DeepMind's AlphaFold 2 system achieved a median Global Distance Test score of approximately 92.4 across all CASP14 targets, far surpassing the next-best group, in a result that computational biologists described as largely solving the 50-year-old protein-folding problem for single-chain proteins.
Blue Brain Project Digital Reconstruction of the Rat Somatosensory Cortex Microcircuitry
On 8 October 2015, Henry Markram and colleagues at the Blue Brain Project published a detailed computational reconstruction of 31,000 neurons and 37 million synapses in a 0.29 mm³ column of juvenile rat somatosensory cortex, creating the first large-scale digital model of a mammalian cortical microcircuit.
Neurorobotics Platform, Human Brain Project
In 2015, the Human Brain Project, a European Commission Flagship Initiative, released its Neurorobotics Platform, a simulation environment allowing researchers to connect large-scale brain models to virtual robot bodies and run closed-loop cognitive experiments without physical hardware.
Spaun: A Large-Scale Functional Brain Model Capable of Performing Multiple Cognitive Tasks
In November 2012, Chris Eliasmith and colleagues at the University of Waterloo published a description of Spaun (Semantic Pointer Architecture Unified Network) in Science, presenting a 2.5-million-neuron brain model capable of performing eight distinct cognitive tasks, including digit recognition, question answering, and list recall.
IBM Simulates 4.5 Percent of Human Brain Activity Using Blue Gene Supercomputer
In November 2011, IBM researchers led by Dharmendra Modha at IBM Research Almaden demonstrated a cortical simulation on the Blue Gene/P supercomputer that modelled approximately 4.5 percent of human-scale neural activity, using 147,456 processors to represent 1.617 billion neurons and 8.87 trillion synapses.
Robot Scientist Adam
In April 2009, researchers at Aberystwyth University and the University of Cambridge published in Science an account of Adam, an automated laboratory system that independently formulated hypotheses about yeast gene function, designed and executed experiments, and interpreted results without human intervention during the cycle.
Nengo Neural Simulation Software Released by the Computational Neuroscience Research Group, University of Waterloo
Researchers at the Computational Neuroscience Research Group (CNRG) at the University of Waterloo developed Nengo, an open-source software environment for simulating large-scale neural systems using the Neural Engineering Framework, providing tools that bridge high-level network specification with low-level neurophysiological detail.
Blue Brain Project Launch
In 2005, neuroscientist Henry Markram at the École Polytechnique Fédérale de Lausanne launched the Blue Brain Project in collaboration with IBM, with the goal of constructing a biologically detailed computational model of the mammalian neocortical column using an IBM Blue Gene/L supercomputer.
First DNA Robot Capable of Bipedal Motion
In April 2004, chemists William Sherman and Nadrian Seeman at New York University reported a bipedal DNA robot whose two 10-nanometre legs walked along a single-stranded DNA track through the sequential addition of 'set' and 'unset' strands, demonstrating programmable nanoscale locomotion.
TEXTAL System for AI-Assisted Automated Protein Model Building
In 2003, Thomas R. Ioerger and James C. Sacchettini at Texas A&M University described TEXTAL, a pattern-recognition system that automatically traced atomic models through crystallographic electron density maps, substantially reducing the manual labour required in protein structure determination.
MOLGEN: AI Planning and Constraint Satisfaction for Molecular Biology Experiment Design
In 1978, Mark Stefik at Stanford University's Heuristic Programming Project developed MOLGEN, an expert system that applied AI planning and constraint-satisfaction techniques to the design of molecular biology experiments, demonstrating that structured reasoning could automate complex scientific problem-solving in genetics and cloning.