## Tags - Part of: - Related: - Includes: - Additional: ## Definitions - [[Biology]] x [[Artificial Intelligence]] x [[Machine learning]] x [[Data science]] x [[Statistics]] ## Main resources - <iframe src="https://en.wikipedia.org/wiki/" allow="fullscreen" allowfullscreen="" style="height:100%;width:100%; aspect-ratio: 16 / 5; "></iframe> ## Landscapes - [AlphaFold - Google DeepMind](https://deepmind.google/technologies/alphafold/) - [AlphaProteo generates novel proteins for biology and health research - Google DeepMind](https://deepmind.google/discover/blog/alphaproteo-generates-novel-proteins-for-biology-and-health-research/) - [Evo: DNA foundation modeling from molecular to genome scale | Arc Institute](https://arcinstitute.org/news/blog/evo) - [PRISM: A foundation model for life’s chemistry | Enveda Biosciences](https://www.envedabio.com/posts/prism-a-foundation-model-for-lifes-chemistry) - OpenFold interpretability [Mechanistic Interpretability - Stella Biderman | Stanford MLSys #70 - YouTube](https://www.youtube.com/watch?v=P7sjVMtb5Sg&t=60s) ## Resources [[Links AI healthcare biology]] [[Links AI biology]] ## AI landscape (may include incorrect information) AI has made significant contributions to solving several major problems in biology. Here are some of the key areas where AI has helped: 1. Protein structure prediction: One of the biggest breakthroughs has been in solving the protein folding problem. DeepMind's AlphaFold AI system was able to predict the 3D structures of nearly all known proteins with high accuracy[1][4]. This has been described as solving "one of biology's grandest challenges for the past 50 years"[4]. Understanding protein structures is crucial for drug development and understanding biological processes. 2. Drug discovery: AI is being used to accelerate various stages of the drug discovery process, including: - Identifying potential drug candidates - Predicting drug-target interactions - Optimizing drug molecules - Designing focused compound libraries[7] 3. Genomics and genetic analysis: AI algorithms are helping analyze large genomic datasets to: - Identify disease-associated genes - Predict effects of genetic variations - Interpret genetic sequences[1] 4. Medical imaging and diagnostics: AI is improving analysis of medical images like X-rays, MRIs, and microscopy data to aid in disease diagnosis[1][2]. 5. Cellular mapping: Researchers are using AI to develop detailed maps of cellular structures and protein interactions, helping connect genotypes to phenotypes[5]. 6. Synthetic biology: AI is assisting in designing novel proteins and biological circuits[1][4]. 7. Precision medicine: AI models are being developed to predict patient responses to treatments based on genetic and other data, enabling more personalized medical approaches[5]. 8. Analyzing biological data: Machine learning algorithms are helping scientists process and extract insights from large, complex biological datasets generated by high-throughput experiments[1][2]. 9. Predicting protein-protein interactions: AI models can predict how proteins interact with each other and with other molecules, which is crucial for understanding cellular processes[6]. 10. Evolutionary biology: AI is being used to analyze genetic data to better understand evolutionary relationships between species and populations[1]. These applications of AI are transforming many areas of biological research, enabling scientists to tackle complex problems and analyze vast amounts of data more efficiently than ever before. The integration of AI into biology is leading to new discoveries and accelerating the pace of research across the field. Citations: [1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9505413/ [2] https://www.sciencedirect.com/science/article/pii/S0092867422007991 [3] https://www.ebi.ac.uk/about/news/perspectives/alphafold-using-open-data-and-ai-to-discover-the-3d-protein-universe/ [4] https://www.diamandis.com/blog/ai-solves-50-year-old-biology-challenge [5] https://www.the-scientist.com/infusion-of-artificial-intelligence-in-biology-71665 [6] https://www.electronicsforu.com/news/biologys-biggest-problem-solved-by-an-ai [7] https://www.receptor.ai/case-studies [8] https://www.scientificamerican.com/article/one-of-the-biggest-problems-in-biology-has-finally-been-solved/