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agbtcode

automl_space

Exploring search spaces for AutoML systems.

compt-ijcai2021-learning-attributed-graph-representation-with-communicative-message-passing-t

Code of our IJCAI2021 paper: "Learning Attributed Graph Representation with Communicative Message Passing Transformer"

comptox-genra-acutetox-comptoxicol

comptox_ai

Artificial Intelligence toolkit for computational toxicology

efficient-tlbo-dl-model-reoptimizations

Efficient transfer learning based bayesian optimization for Hyperparameter optimization

enhancing-toxicological-testing-through-machine-learning

Enhancing Toxicological Testing through Machine Learning

hybridtox2d

In recent times, toxicological classification of chemical compounds is considered to be a grand challenge for pharma-ceutical and environment regulators. Advancement in machine learning techniques enabled efficient toxicity predic-tion pipelines. Random forests (RF), support vector machines (SVM) and deep neural networks (DNN) are often ap-plied to model the toxic effects of chemical compounds. However, complexity-accuracy tradeoff still needs to be ac-counted in order to improve the efficiency and commercial deployment of these methods. In this study, we implement a hybrid framework consists of a shallow neural network and a decision classifier for toxicity prediction of chemicals that interrupt nuclear receptor (NR) and stress response (SR) signaling pathways. A model based on proposed hybrid framework is trained on Tox21 data using 2D chemical descriptors that are less multifarious in nature and easy to calcu-late. Our method achieved the highest accuracy of 0.847 AUC (area under the curve) using a shallow neural network with only one hidden layer consisted of 10 neurons. Furthermore, our hybrid model enabled us to elucidate the inter-pretation of most important descriptors responsible for NR and SR toxicity.

mindware--open-source-automl-system

An efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.

ml-organ-tox

Data-driven chemical-induced toxicity prediction by machine learning using chemical and bioactivity data

open-box

Generalized and Efficient Blackbox Optimization System.

sourceforpub

Source code for all published work.

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