Artificial intelligence(AI)is revolutionizing the traditional paradigm of drug development at an unprecedented pace.With the rapid advancement of technologies such as deep learning and machine learning,AI has demonstr...Artificial intelligence(AI)is revolutionizing the traditional paradigm of drug development at an unprecedented pace.With the rapid advancement of technologies such as deep learning and machine learning,AI has demonstrated substantial potential in various aspects of pharmaceutical research,including drug target identification,molecular design,and clinical trial optimization(Figure 1).Industry reports suggest that AI has the potential to reduce the drug development timeline from the conventional 10–15 years to 2–3 years,while also slashing development costs by billions of dollars.This article provides a comprehensive analysis of the current applications and future trends of AI in drug development and discovery,focusing on three dimensions:current hotspots,challenges,and future directions.展开更多
Recently,the Royal Swedish Academy of Sciences announced that one-half of the 2024 Nobel Prize in Chemistry was awarded to David Baker for computational protein design,and the other half was awarded jointly to Demis H...Recently,the Royal Swedish Academy of Sciences announced that one-half of the 2024 Nobel Prize in Chemistry was awarded to David Baker for computational protein design,and the other half was awarded jointly to Demis Hassabis and John Jumper for protein structure prediction.Proteins are the molecule tools of life.To carry out their functions,many proteins need to fold into particular three-dimensional(3D)structures,which are programmed by the amino acid sequences of the proteins[1].The goal of protein structure prediction is to predict the 3D structures from given amino acid sequences.Conversely,protein design aims to identify amino acid sequences that achieve desired structural or functional goals.展开更多
基金supported by the Centrally Guided Local Science and Technology Development Project(2024ZYD0043).
文摘Artificial intelligence(AI)is revolutionizing the traditional paradigm of drug development at an unprecedented pace.With the rapid advancement of technologies such as deep learning and machine learning,AI has demonstrated substantial potential in various aspects of pharmaceutical research,including drug target identification,molecular design,and clinical trial optimization(Figure 1).Industry reports suggest that AI has the potential to reduce the drug development timeline from the conventional 10–15 years to 2–3 years,while also slashing development costs by billions of dollars.This article provides a comprehensive analysis of the current applications and future trends of AI in drug development and discovery,focusing on three dimensions:current hotspots,challenges,and future directions.
文摘Recently,the Royal Swedish Academy of Sciences announced that one-half of the 2024 Nobel Prize in Chemistry was awarded to David Baker for computational protein design,and the other half was awarded jointly to Demis Hassabis and John Jumper for protein structure prediction.Proteins are the molecule tools of life.To carry out their functions,many proteins need to fold into particular three-dimensional(3D)structures,which are programmed by the amino acid sequences of the proteins[1].The goal of protein structure prediction is to predict the 3D structures from given amino acid sequences.Conversely,protein design aims to identify amino acid sequences that achieve desired structural or functional goals.
