Efficient identification of novel anti-glioma lead compounds by machine learning models

Arylsulfonamide chalcones as alternatives for fuel additives: antioxidant activity and machine learning protocol studies

“…145 Arylsulfonamide chalcones are compounds that satisfy these criteria, with a synthesis in ≤5 steps with solubility in polar and non-polar solvents and with high purity. 146,147 In addition, it is important to consider (1) the cost, (2) availability, (3) sustainability, and (4) the potential formation of nitrogen and sulfur oxides during combustion. Although arylsulfonamide chalcones have some challenges, further research on chalcone-based compounds will inform the applicability of chalcones as biofuel additives, as well as their comparison with existing antioxidants.…”

Automated Framework for Developing Predictive Machine Learning Models for Data-Driven Drug Discovery

“…3,4 One of the primary application areas for ML in drug discovery is to predict chemicals lacking of biological data. [5][6][7][8] Over the past decade, there has been a remarkable increase in the amount of available bioassay data in repositories such as ChEMBL 9 and PubChem 10 owing to the emergence of new experimental techniques such as high-throughput screening (HTS). 11,12 This rapid increase in publicly available data has allowed the training of ML algorithms to guide the development of lead compounds in drug discovery as well as in the assessment of chemical safety of untested compounds.…”