Deep Learning and Ligand Optimization
The integration of deep learning in computational chemistry has revolutionized the field, particularly in the optimization of ligand. This approach leverages advanced ML algorithms to predict and design molecular structures that can bind to specific targets with high affinity and specificity.
AlphaFold2: A Breakthrough in Protein Structure Prediction
One of the most notable advancements in this domain is the development of AlphaFold2 by Google DeepMind. AlphaFold2 has successfully predicted the three-dimensional structures of nearly all known proteins, overcoming a challenge that has perplexed scientists for over 50 years. This achievement was recognized with the 2024 Nobel Prize in Chemistry awarded to David Baker, Demis Hassabis, and John M. Jumper. The ability to predict protein structures accurately is crucial for understanding protein-ligand interactions, which is a fundamental aspect of drug discovery. For more details, refer to the 2024 Nobel Prize in Chemistry.
Google DeepMind’s AlphaFold Server
Google DeepMind has also released a new version of its AlphaFold AI model, along with a free online tool called AlphaFold server for scientists to test their hypotheses. This tool allows researchers to predict the behavior of microscopic proteins, which is crucial for designing drugs and compounds that target specific diseases. The application of AI in drug discovery and development has the potential to significantly speed up the drug discovery pipeline, leading to faster development of life-changing treatments. More information can be found in the Google DeepMind unveils next generation of drug discovery AI model.
AI in Drug Discovery: Isomorphic Labs
Isomorphic Labs, a spin-off from DeepMind, has inked deals with pharmaceutical giants Eli Lilly and Novartis for drug discovery. Leveraging advanced AI to predict protein structures and accelerate drug development, Isomorphic Labs aims to create more effective treatments for various diseases. This collaboration highlights the transformative potential of AI in drug discovery and the strengths of such partnerships. For more details, refer to the Isomorphic inks deals with Eli Lilly and Novartis for drug discovery.
EvolutionaryScale: Generating Novel Proteins
EvolutionaryScale, backed by Amazon and Nvidia, has raised $142 million for its AI models that generate novel proteins for scientific research, specifically drug discovery and materials science. Their flagship product, ESM3, can ‘reason over’ protein sequence, structure, and function, enabling it to generate novel proteins with specific properties. This advancement in protein generation could revolutionize drug discovery and protein engineering, leading to faster and cheaper development of new therapies and materials. More information can be found in the EvolutionaryScale raises $142M for protein-generating AI.
AI and Transomics: Pepper Bio
Pepper Bio is utilizing transomics technology to address untreated data. The benchmark, MUSE (Machine Unlearning Six-way Evaluation), tests an algorithm’s ability to prevent a model from spitting out training data verbatim and eliminate the model’s knowledge of that data. However, the study found that while unlearning algorithms can make models forget certain information, they also hurt the models’ general question-answering capabilities, presenting a trade-off. For more details, refer to the Making AI models ‘forget’ undesirable data hurts their performance.
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