In Silico Drug Discovery (2019/2020)[FLEX]
Weekly outline
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In Silico Drug Discovery
A major contributor of new leads to the drug discovery process is large-scale, often database-driven, calculation based on modelling ligand-macromolecular interactions or searches based on properties derived from pre-existing chemical entities. In this course these database mining techniques, along with allied chemical fragment-based approaches will be explored.
Learning Outcomes
At the end of this course students should be able to:
- Describe the major aspects of a docking application, including the methods for initial site point generation, and the various approaches to pose fitting.
- Distinguish between force field/enthalpy-based and knowledge-based pose scoring methods.
- Understand the properties that increase the value of compound databases.
- Have a clear understanding of the values and problems associated with multiconformer vs. single conformer chemical databases.
- Describe the principles behind QSAR analysis and provide examples of how QSAR can be used in decision making.
- Describe the major classes of similarity searching algorithms, and understand the types of problem that such programs can be applied to.
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How to use PyMOL (including the molecule builder functions in part 3).
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This week will introduce the general concepts of in silico drug discovery, including structure- and ligand-based virtual screening, design and management of small molecule libraries, and quantitative structure-activity relationships.
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This week we will look at the difference between empirical/force-field based potentials (Autodock) and knowledge-based potentials (DSX).
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This is the structure of the ligand bound to the HIV protease from PDB 1HSG
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This week we will look more closely at the practicalities of performing a structure-based virtual screen, and the use of QSAR model building to guide the selection and design of lead compounds.
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This week will be somewhat lighter in content to allow you to make a start on the final assessment
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This week is reserved for the completion of your assignment report
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Perform a Virtual High Throughput Screen of Your Target (50%)
Your task is to perform a structure- and ligand-based virtual screen using your choice of SARS-CoV-2 target. Order the virtual compounds according to the appropriate scoring algorithms. Reprioritise the top 100 compounds from each of the two ranked lists of virtual hits that result from these screens using Stardrop.
Summarise your methodology (and your reasoning behind it) and results in a 1000-word (excluding figure legends, tables, references etc.) .doc report that includes the top 10 compounds from each of the two virtual screening methods and discusses their relative merits. Also provide a Pymol .pse file containing these compounds docked into the pocket of your target structure and refer to this in your report to aid you in your description of the protein-ligand interactions.
So, to summarise, your submission will consist of:
1. A Word document
2. A Pymol session
The deadline for the Assessed Report is Monday 18th May 16:00 (BST) 2020.
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