Intro to Drug Discovery for Computer Scientists

Name: Intro to Drug Discovery for Computer Scientists
Rating: 4.0 (97 reviews)

What roles could computer scientists play in drug discovery

Created byAgnes Huang, PhD

Last updated 5/2021

English

What you'll learn

History of modern drug discovery and the role of computations
Three grand challenges highly relevant to the field of drug discovery: CASP, D3R, SAMPL
Computational models and software used in drug candidate discovery
Insider-opinions working in big pharma, biotech or scientific software companies as computer scientists

Course content

4 sections • 15 lectures • 1h 18m total length

Course Overview2:16
Introduction of target audience and overview of the course.
Start-of-course Survey

A Brief History of Medicine2:57
Examples of ancient and modern medicines.
Modern Drug Discovery R&D2:10
Understand the basic processes of modern drug discovery research and development.
Roles of Data and Computations6:13
Overview of data-driven integration and roles of computer scientists in the drug discovery industry.
Phenotypic Drug Discovery2:48
Alternative method for drug discovery with respect to target-based drug discovery.
Quiz for Drug Discovery Introduction

Why are we interested in Grand Challenges?1:04
The significance of grand challenges.
Grand Challenges in the Context of Drug Discovery4:36
Two central problems to be resolved in drug discovery related grand challenges.
CASP for Protein Structure Prediction: 14:04
Background information about protein structures and protein folding.
CASP for Protein Structure Prediction: 29:29
Methods in CASP and advances made by AlphaFold via deep learning.
SAMPL and D3R for Protein and Ligand Modeling3:14
A brief overview of the SAMPL and D3R to assess computational methods in protein and ligand modeling.
Quiz for Grand Challenges in Computational Drug Design

Overview of CADD2:32
Recap of drug discovery process with more details and the role of CADD.
Computational Representations of chemical molecules8:48
Computational representations of chemical structures and applications using computational models.
Computer-Aided Ligand-based Drug Discovery10:04
Techniques used in ligand-based drug discovery, including shape similarity: rapid overlay of chemical structure (ROCS), Pharmacophore modeling and 3D-Quantitative structure–activity relationship (QSAR). Followed by one example of ROCS.
Workflow of Structure-based Drug Design5:42
Overview of structure-based drug discovery (SBDD).
Computer-Aided Structure-based Drug Discovery12:13
Techniques and strategies used in structure-based drug discovery, including Virtual Screening (VS), De Novo Design, Fragment-base Methods, Molecular Docking, Molecular Dynamics, and Free Energy Calculation. Followed by one example of VS.
Quiz 3: Quiz for CADD

Requirements

This course assumes high school knowledge in chemistry and biology.
This course assumes that students feel comfortable talking about some terminologies in those disciplines.
The most importantly, this course assumes some initial curiosity in students wanting to see how computer science and computational models have been used to help people push drug discovery forward.

Description

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#Welcome new and existing students! This course is now finished! #

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This course is developed for computer science, computer engineering college students and/or professionals in this field, who want to explore and seek opportunities in healthcare industry R&D, specifically drug discovery.

This course will first go through the history of modern drug discovery with an emphasis on computational approaches including Computer-Aided Drug Discovery (CADD) and Machine Learning (ML). It will introduce to you three grand challenges highly relevant to the field of drug discovery, the prediction of protein structures CASP, drug design resource grand challenge D3R and modeling of protein-drug interactions SAMPL. Among them, computational models and software used in drug candidate discovery will be introduced more extensively because that’s where most of my experience and expertise lie in. At last, you will get a chance to learn from people working in the field of drug discovery to see what role computer scientists are playing working in the related industry, including big pharma Research & Development, biotech company or software company developing drug design tools.

At the end of this course, you will establish basic knowledge about the process of drug candidate discovery. You will be able to name some challenging domain problems and current cutting-edge solutions to them. But as a fast-growing field, the current cutting-edge can be replaced as more techniques are developed. Therefore, the ultimate goal of this course is to motivate your interest in drug discovery, and get the point across that computer scientists can help make a huge difference in the field of drug discovery and design. Hopefully after taking this course, your interest level in this field would be elevated and would like to delve into it more deeply. It would be of great value and worthwhile to build your career toward this direction.

Who this course is for:

Undergraduate and graduate students majoring in computer science, computer engineering
Computer scientists and professionals in related field
Anyone with curiosity and interests in computational drug discovery

Intro to Drug Discovery for Computer Scientists

What you'll learn

Explore related topics

Course content

Introduction1 lecture • 2min

Introduction to Modern Drug Discovery4 lectures • 14min

Blinded Grand Challenges5 lectures • 22min

Computer-Aided Drug Discovery5 lectures • 39min

Requirements

Description

Who this course is for: