Podcast
Questions and Answers
What is the aim of virtual screening?
What is the aim of virtual screening?
When is virtual screening employed?
When is virtual screening employed?
What methods can be used to assess 'drug-likeness'?
What methods can be used to assess 'drug-likeness'?
What sparked interest in the concept of 'drug-likeness'?
What sparked interest in the concept of 'drug-likeness'?
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What is the 'Rule of Five' (ROF) primarily concerned with?
What is the 'Rule of Five' (ROF) primarily concerned with?
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What is the main reason for using different functions for docking and scoring?
What is the main reason for using different functions for docking and scoring?
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What is ΔGint in the context of scoring functions for docking?
What is ΔGint in the context of scoring functions for docking?
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What does the lipophilic term in the linear scoring function introduced by Bohm (1994) depend on?
What does the lipophilic term in the linear scoring function introduced by Bohm (1994) depend on?
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What is a practical tip for structure-based virtual screening?
What is a practical tip for structure-based virtual screening?
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What is the main consideration when defining the binding site for docking?
What is the main consideration when defining the binding site for docking?
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What are the criteria outlined by the 'rule of five' for poor absorption?
What are the criteria outlined by the 'rule of five' for poor absorption?
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What is the distinct feature of 'lead-likeness' compared to 'drug-likeness'?
What is the distinct feature of 'lead-likeness' compared to 'drug-likeness'?
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What method aims to predict the 3D structure of intermolecular complexes?
What method aims to predict the 3D structure of intermolecular complexes?
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What are the input, hidden, and output nodes in the feed-forward neural network used for predicting drug-likeness?
What are the input, hidden, and output nodes in the feed-forward neural network used for predicting drug-likeness?
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What do more recent algorithms for docking take into account?
What do more recent algorithms for docking take into account?
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Study Notes
Drug-Likeness and Structure-Based Virtual Screening
- The "rule of five" outlines criteria for poor absorption, including molecular weight > 500, logP > 5, > 5 H-bond donors, and > 10 H-bond acceptors.
- 70% of "drug-like" compounds had specific ranges for H-bond donors, H-bond acceptors, rotatable bonds, and rings.
- A feed-forward neural network had 92 input nodes, 5 hidden nodes, and 1 output node, correctly predicting drug-likeness in molecules.
- Decision trees were used to correctly classify 91.9% of drugs but with a 34.3% false positive rate for non-drugs.
- "Lead-likeness" is distinct from "drug-likeness" and involves increasing molecular complexity during lead optimization.
- The "rule of three" is associated with lead-likeness and is used in fragment-based approaches to drug discovery.
- The number of protein crystal structures has significantly increased, driving interest in structure-based methods for virtual screening.
- Protein-ligand docking aims to predict the 3D structure of intermolecular complexes and involves exploring possible geometries and scoring poses.
- The DOCK method involves constructing a negative image of the binding site using overlapping spheres and matching ligand atoms to sphere centers.
- More recent algorithms for docking take ligand orientational and conformational degrees of freedom into account, using methods like Monte Carlo and genetic algorithms.
- Scoring functions for protein-ligand docking aim to predict binding geometry and free energies of association, with some programs correctly predicting binding geometry in over 70% of cases.
- The same function is ideally used for both docking ligands and predicting their free energies of binding.
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Description
Test your knowledge of drug-likeness, lead-likeness, and structure-based virtual screening with this quiz. Explore key concepts such as the "rule of five," neural network predictions, decision tree classifications, protein-ligand docking, and scoring functions for virtual screening.