Machine Learning in Chemistry

the combination of machine learning and chemistry has made significant progress. Researchers are using advanced models like CNNs and RNNs for tasks such as creating new drugs, predicting toxicology, and modeling quantitative structure-activity relationships. The pursuit of models that are interpretable and explainable is becoming more important, giving scientists a better understanding of why predictions are made. Additionally, the use of multi-modal data and the development of transfer learning techniques are expanding what can be achieved in predicting material properties and optimizing synthesis planning. These recent trends highlight the growing collaboration between machine learning and chemistry, pushing scientific research into new areas and influencing the future of chemistry research. <a href="https://pub.towardsai.net/machine-learning-in-chemistry-87e6ff866026">Read More</a>