写的:
机器学习正在改善澳门葡京赌博游戏的生产方式, screen and evaluate molecules as potential candidate medicines.
药物发现的根本挑战
The number of possible drug-like chemicals greatly outnumbers the stars in the universe. Developing new medicines therefore requires us to consider very large numbers of potential molecules (the ‘molecular space’) to identify suitable candidates that can then be investigated in more depth for their utility as potential therapeutics.
来帮助澳门葡京赌博游戏缩小对主要候选分子的Search范围, we combine approaches such as high-throughput screening and computational chemistry. These processes start with a very large number of molecules and progressively ‘funnel’ the focus towards an ever-smaller number of molecules with the desired medicinal effects and safety profiles.
Studying potential molecules at the beginning of this funnel easily generates a lot of data, 但提供的见解有限. 相反, as the analysis progresses along the funnel and focuses on fewer molecules more deeply, it becomes more expensive but provides increasingly meaningful data.
为了克服这一挑战, we are continually improving and applying machine learning models, 例如图神经网络和变压器模型, to better understand the potential molecular space we want to investigate and predict the likely chemical properties of candidate drugs.
Using neural networks to predict molecule properties and identify candidate drugs
考虑到大量的潜在分子, the feasibility of assessing them all for varied properties such as absorption, 体内分布, 新陈代谢, 消除, 功效, 安全几乎是不可能的. Machine Learning tools such as graph neural networks are already in common use to help predict properties of large numbers of molecules.
Our scientists have now paired graph neural networks with ‘transfer learning’ – a type of machine learning strategy that can store knowledge from one task and can then ‘transfer’ these learnings to a different, 相关的问题.1 We have used transfer learning to store the knowledge from datasets that are large and easily generated at the 早期阶段 药物发现漏斗(但是), 他们自己, provide limited insights) to improve the predictive performance at the 后期 of the funnel – where datasets are more expensive to generate but can provide deeper insights.
第一次, 据澳门葡京赌博游戏所知, we have demonstrated how transfer learning with graph neural networks can use data from the full funnel to improve molecular property prediction, enabling scientists to make smarter decisions about which molecules to progress, particularly when there is not a lot of high-quality data available initially. 此外, our study highlights limitations of standard graph neural networks and proposes solutions that enable transfer learning.
在R中嵌入数据科学和人工智能&D
数据科学 and AI are helping us to analyse and interpret large quantities of data at all stages of drug discovery and 发展. By combining traditional chemistry high-throughput screening with AI and machine learning approaches, we are able to study increasingly diverse and promising molecules – helping us better predict their molecular function and suitability as a medicine. 通过在R中嵌入数据驱动方法&D, 澳门葡京赌博游戏正在加速澳门葡京赌博游戏的设计, develop and make the next generation of therapeutics for patients.
