Message-Passing Neural Networks for Molecular Property Prediction Using Chemprop скачать в хорошем качестве

Message-Passing Neural Networks for Molecular Property Prediction Using Chemprop

2022.04.13, Kevin Greenman, Massachusetts Institute of Technology (MIT) Chemprop demo tool can be found at: https://nanohub.org/tools/chempropdemo Part of Hands-on Data Science and Machine Learning Training Series at: https://nanohub.org/groups/ml/handson... Table of contents below. Chemprop is an open-source implementation of a directed message passing neural network (D-MPNN) that has been demonstrated to be successful in predicting a variety of molecular properties, including solvation properties, optical properties, infrared spectra, and toxicity. The framework has also been used to predict reaction properties and to identify new antibiotics. Chemprop can be used via a command-line interface or within a Python script or Jupyter notebook. In this tutorial, we will discuss the D-MPNN algorithm and several successful use cases, followed by a demonstration of some of Chemprop’s core functionalities (e.g. training and testing models for single- and multiple-molecule properties and reactions, transfer learning, and estimating uncertainty). Table of Contents: 00:00 Message-Passing Neural Networks for Molecular Property Preduction Using Chemprop 01:41 What is Chemprop? 02:29 Acknowledgements 03:13 What we'll cover today 03:49 How can we represent a molecule as a vector? 05:10 D-MPNN method 05:49 In Contrast – Fixed Encodings 06:31 MPNN – Trainable Encodings 06:37 Chemprop Structure 07:52 Dataset Types and Targets 08:16 Data Input Formatting 09:04 Training 09:38 Major Hyperparameters 10:07 Data Splitting 10:52 Data Splitting 11:27 Loss Functions 11:45 Multiple Molecules 12:45 Predicting 13:08 Fingerprinting 13:48 Fingerprint Type 14:03 Heuristics 15:52 Features Not Covered Today 16:47 Papers Using Chemprop 17:13 Papers Using Chemprop (cont) 17:32 Resources 18:20 Chemprop This presentation and related downloads can be found at: https://nanohub.org/resources/36082