New machine learning techniques boost predictions for virtual drug screening with less data using kernel methods

Recommendable! Sounds promising!

"... They came up with the first “transfer learning” techniques for kernel methods that can be successfully applied to large-scale datasets. ...
The researchers analyzed performance of their transfer learning algorithms on two massive Broad datasets, one from the Connectivity Map (CMAP) and the other from the Cancer Dependency Map (DepMap). These datasets describe the effects of drugs on cancer cell lines  across millions of drug and cell line combinations. The team trained their kernel method algorithms to predict either the genes expressed by a certain cell type after it was treated with a certain drug (using the CMAP dataset), or the proportion of cancer cells that survived after treatment with the same drug (using the DepMap dataset). ... Two additional advantages  of kernel methods are that they provide interpretability as well as a quantification of how uncertain the model is on a given prediction. ..."

From the abstract:

"Transfer learning refers to the process of adapting a model trained on a source task to a target task. While kernel methods are conceptually and computationally simple models that are competitive on a variety of tasks, it has been unclear how to develop scalable kernel-based transfer learning methods across general source and target tasks with possibly differing label dimensions. In this work, we propose a transfer learning framework for kernel methods by projecting and translating the source model to the target task. We demonstrate the effectiveness of our framework in applications to image classification and virtual drug screening. For both applications, we identify simple scaling laws that characterize the performance of transfer-learned kernels as a function of the number of target examples. We explain this phenomenon in a simplified linear setting, where we are able to derive the exact scaling laws."

New machine learning techniques boost predictions for virtual drug screening with less data | Broad Institute ... researchers have found a way to improve kernel methods, which are a simpler alternative to neural networks, for biomedical applications. 

Transfer Learning with Kernel Methods (open access)

Fig. 1: Our framework for transfer learning with kernel methods for supervised learning tasks.