Improving Deep Learning Models in Drug Discovery (Merck)
Sprache der Bezeichnung:
Englisch
Original Kurzfassung:
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.
Forschungs-