## Description

The discovery and development of new drugs is in a stage of rapid acceleration, due to the confluence of four factors: easy access to big data through internet, an explosion of biological structural information, advanced modeling, and extremely cheap computer power through clouds. The course will briefly describe the mathematical background of a few of the most relevant modern computational techniques.

After the course, the student will have learned the following (1) The organization of pharmaceutical molecular research in the discovery pipeline workflow paradigm, and the relevance of computations therein (target definition, lead optimization, clinical trials analysis). (2) How to setup and perform empirical structural property calculations using QeQ and forcefields methods. (3) How to rationalize a choice of molecular descriptors for statistical clustering and quantitative structure property relations. (4) The mathematical background of Machine (deep) Learning, and how to set up such calculations using Google Tensfor Flow.

The course is in the form of 8 sessions, of each 2 times 45 minute lectures, followed by a one hour block for making exercises.

At the end of the course, the student will have a good overview of the most common calculation methodologies. The student will be able to digest presentations from recent conferences, some of which will be used in the course as supporting materials.

Prior knowledge of the following theoretical methods is mandatory: quantum chemistry, statistical thermodynamics, non-linear optimization, and linear algebra.

The student is strongly advised to have achieved good understanding of Python through self-study or additional courses.

The student should be aware that the focus is on the mathematical background of the various methods.

## Mode of Instruction

Lectures and exercises.

## Literature

The teacher will provide a reader (collection of papers), powerpoints and a list of questions and answers.

## Examination

Exam is 3 hours closed-book, with one question per lesson.

## Contact Information

j.fraaije@chem.leidenuniv.nl

## Additional Info

Some of the teaching material is based in the course ‘Molecular Modeling’, given in previous years by the same teacher. Presence at the lectures and workshop is obligatory.