Deep Learning in the Optimization Stage

In the optimization stage, the goal is to understand and improve properties of small molecule compounds.

Deep learning can enhance prediction accuracy and efficiency.

Directed Message Passing Neural Networks (D-MPNNs) are deep learning models that learn molecular structures by passing information along directed edges in the molecular graph.

Learned vs. fixed molecular representations. Traditional models use fixed descriptors, while models like Chemprop use learned representations tailored to the prediction task. (Adapted from Yang et al., JCIM 2019)

Chemprop, developed at MIT, uses D-MPNNs for predicting molecular properties.

Note: Chemprop is not well-suited for peptides.

Last updated on Jan 1, 0001