Functionally Regionalized Knowledge Transfer for Low-resource Drug Discovery
Authors: Huaxiu Yao, Ying Wei, Long-Kai Huang, Ding Xue, Junzhou Huang, Zhenhui (Jessie) Li
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we empirically evaluate the effectiveness of FRML on two diverse drug discovery tasks: drug activity prediction and ADMET property prediction. Empirical results on both virtual screening and ADMET prediction validate the superiority of FRML over state-of-the-art baselines powered with interpretability in assay relationship. |
| Researcher Affiliation | Collaboration | Huaxiu Yao1 , Ying Wei2, Long-Kai Huang3, Ding Xue3 Junzhou Huang4, Zhenhui Li5 1Stanford University, 2City University of Hong Kong, 3 Tencent AI Lab 4University of Texas at Arlington, 5Pennsylvania State University |
| Pseudocode | Yes | Algorithm 1 Meta-training Process of FRML |
| Open Source Code | No | The paper does not contain any statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | For drug activity prediction, we use the dose-response activity assays from ChEMBL[1], where 4,276 assays are selected in this problem. The AMDET Prediction problem is constructed by combining 4 benchmark datasets from the Molecule Net [36] with biophysiology and physiology targets. The 4 datasets are MUV [29], SIDER [11], Tox21 and Tox Cast [27]. |
| Dataset Splits | Yes | Here, we randomly sample 100 assays as the meta-testing set, 76 assays as the meta-validation set, and the rest of assays for meta-training. |
| Hardware Specification | No | The paper does not specify any particular hardware components such as GPU models, CPU types, or memory used for running the experiments. It only generally refers to the computational environment. |
| Software Dependencies | No | The paper mentions RDKit [12] as an implementation tool ('implemented in RDKit [12]') but does not provide a specific version number for it or any other software dependencies in the main text. |
| Experiment Setup | Yes | We construct a neural network consisting of two fully connected layers as the predictive model, which also serves as the base learner f. We denote the weights for the base learner f to be θ. With the base learner f, we introduce gradient-based meta-learning as the backbone meta-learning framework, which regards the initialization θ0 for the base learner as the transferable knowledge. Here α denotes the learning rate for assay adaptation. where β is the learning rate for meta-updating. where τ is the temperature and qml ml+1 i is sampled from the Gumbel distribution... where the hyperparameter λ balances between two losses... Detailed hyperparameters for both applications are listed in Appendix D. |