15 Questions
What is the aim of virtual screening?
To score, rank, and/or filter a set of structures using computational procedures
When is virtual screening employed?
When analyzing the results of an experiment, such as a HTS run
What methods can be used to assess 'drug-likeness'?
Use of substructure filters and analysis of simple properties such as molecular weight, number of rotatable bonds, and calculated logP
What sparked interest in the concept of 'drug-likeness'?
Observation that combinatorial chemistry and HTS did not lead to expected improvements in identifying lead molecules
What is the 'Rule of Five' (ROF) primarily concerned with?
Assessing drug-likeness based on specific properties such as molecular weight, number of rotatable bonds, and calculated logP
What is the main reason for using different functions for docking and scoring?
To accommodate the large number of orientations generated during a typical docking run
What is ΔGint in the context of scoring functions for docking?
Contribution from protein–ligand interactions
What does the lipophilic term in the linear scoring function introduced by Bohm (1994) depend on?
Contact surface area (Alipo) between protein and ligand involving non-polar atoms
What is a practical tip for structure-based virtual screening?
Apply filters prior to the docking to eliminate undesirable or inappropriate structures
What is the main consideration when defining the binding site for docking?
Avoiding too small a binding site to prevent discarding potential ligands
What are the criteria outlined by the 'rule of five' for poor absorption?
Molecular weight > 500, logP > 5, > 5 H-bond donors, > 10 H-bond acceptors
What is the distinct feature of 'lead-likeness' compared to 'drug-likeness'?
Involves increasing molecular complexity during lead optimization
What method aims to predict the 3D structure of intermolecular complexes?
Protein-ligand docking
What are the input, hidden, and output nodes in the feed-forward neural network used for predicting drug-likeness?
92 input nodes, 5 hidden nodes, 1 output node
What do more recent algorithms for docking take into account?
Ligand orientational and conformational degrees of freedom
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.
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.
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