kntkb / 3d-mil-qsar-test

Cloned from https://github.com/cimm-kzn/3D-MIL-QSAR.git

QSAR modeling based on conformation ensembles using a multi-instance learning approach

Cloned and modified from https://github.com/cimm-kzn/3D-MIL-QSAR.git

This repository containes the Python source code from the paper "QSAR modeling based on conformation ensembles using a multi-instance learning approach" with additional features.

• Support other ML methods to compare with Multi-Instance Learning approach.
• Add configuration file to run experiments easier.
• Add statistical report

Overview

Our research focuses on the application of Multi-Instance Learning (MIL) in QSAR modeling. In Multi-Instance Learning, each training object is represented by several feature vectors (bag) and a label. In our implementation, an example (i.e., a molecule) is presented by a bag of instances (i.e., a set of conformations), and a label (a bioactivity value) is available only for a bag (a molecule), but not for individual instances (conformations). Both traditional MI algorithms and MI deep neural networks were used for model building.