First semester

Lecture 1  Introduction

Lecture 2  Search, extraction, analysis and filtering of data. Overview of most popular chemical databases.

Lecture 3  Chemical structure representation. Generation of 3D structures. Chemical file formats.

Lecture 4  Descriptors and graph kernels in chemistry and materials science.

Lecture 5  Chemography: molecular graphs-based and network-based methods

Lecture 6  Chemography: descriptors (dimensionality reduction)-based methods

Lecture 7  Development of models: data preparation, model development and validation. Main types of machine learning (supervised, unsupervised, semisupervised, multi-task). Classification methods, ensembles of classifiers, metric learning.

Lecture 8  Regression methods, dimensionality reduction, online resources

Second semester

Lecture 1 Preparation of databases for virtual and experimental screening ("Fail early, fail fast, fail cheap"), design of chemical libraries.

Lecture 2  3D QSAR.

Lecture 3 Molecular docking.

Lecture 4  Molecular design De Novo. 

Lecture 5 Materials informatics (part I).