Biology
AI milestones in biology, part of life sciences.
6 milestones
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.
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.
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.