Improving Deep Learning Models in Drug Discovery
Term: 5/2016 - 4/2019 (36 months)
In this project, we aim to develop improvements to the current Deep Learning methods for drug discovery. These improvements could affect any of the following algorithmic components:
- Activation functions: Sigmoids, Rectified Linear Units, Exponential Linear Units, Leaky Rectified Units, etc.
- Architecture: Standard, Residual Networks, Highway Networks, deep or broad architectures, connectivity of the architecture, etc.
- Regularization techniques: Dropout, weight decay, etc.
- Learning techniques: Online learning, gradient descent, stochastic gradient descent, etc.
- Initialization strategies * Strategies countering the vanishing gradient problem
- Representation of the chemical input data: 2D&3D chemical descriptors, ECFP, DFS, toxicophore descriptors, molecular graph convolutions, etc.