A new algorithm for accurate drug property predictions
Neural networks and deep learning have been successfully applied to tackle problems in drug discovery with increasing accuracy over time. There are still many challenges and opportunities to improve molecular property predictions with satisfactory accuracy even further.
Recently, the team of AlphaMol developed a deep-learning architecture model, namely Bidirectional long short-term memory with Channel and Spatial Attention network (BCSA), of which the training process is fully data-driven and end to end. With this new algorithm, the physical property predictions for drug molecules improved noticeably with the correlation factor up to 0.99.
Recently, the team of AlphaMol developed a deep-learning architecture model, namely Bidirectional long short-term memory with Channel and Spatial Attention network (BCSA), of which the training process is fully data-driven and end to end. With this new algorithm, the physical property predictions for drug molecules improved noticeably with the correlation factor up to 0.99.
Reference
Accurate Physical Property Predictions via Deep Learning. Molecules (2022), 27(5), 1668; https://doi.org/10.3390/molecules27051668