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Small-molecule drug discovery in the age of AI
AI + Medicine Newsletter 2023-12-12
Small Molecule
A review nicely summarized the recent advances of deep learning (DL) in small-molecule drug discovery. From the methodology perspective, small molecules can be represented in SMILES format or molecular graph (the review missed molecular fingerprint, which is also a 1D string like SMILES), thus string-based and graph-based DL algorithms are most commonly used in predicting small molecule properties.
Graph-based methods, like Graph convolutional network, inherit the pooling layers from conventional convolutional network. A recent study found that attention-based pooling is more effective than max, sum, mean, and physics-aware pooling methods.
To design new small molecules, typical generative models such as RL, diffusion, GAN, and autoencoder can be used (Figure 2). Like large language models, all these models learn the “knowledge” by pertaining on a large, unlabeled molecular dataset. Instead of learning from small molecules, a new study explored chemical reactions and built a foundation model to learn the reaction rules of molecules. Given some seed molecules, the model can generate synthesizable and high-quality drug-like structures based on the learned reaction rules.
