Introduction to Virtual Screening
In the field of drug discovery, virtual screening (VS) has become a prominent computational technique for identifying promising drug candidates from vast molecular libraries. Virtual screening accelerates the early-stage drug discovery process by predicting potential bioactive compounds before laboratory testing, reducing costs and time.
Virtual screening approaches are broadly classified into two categories:
1. Ligand-Based Virtual Screening (LBVS)
LBVS is based on the similarity principle which suggest that structurally similar molecules exhibit similar biological activity. This approach does not relay on the knowledge of target protein structure; instead, it relies on information from known active small molecules to identify new potential hits. Typical LBVS methods consist of:
- 2D Similarity Searching (e.g., fingerprint-based methods)
- 3D Shape-Based Screening
- Pharmacophore Modeling
- QSAR (Quantitative Structure-Activity Relationship) Modeling
2. Structure-Based Virtual Screening (SBVS)
This approach uses the three-dimensional structure of the target protein, obtained from experimental techniques like X-ray crystallography or NMR spectroscopy or Cryo-electron microscopy to identify and design compounds that can bind effectively
This process accelerates the discovery of potential drug candidates by focusing on molecules with a high likelihood of effective binding and biological activity. Key techniques include:
- Molecular Docking (rigid, flexible, and induced-fit docking): to predict how well each molecule fits within the binding site, considering factors like shape complementarity, electrostatics, and hydrophobic interactions.
- Fragment-Based Screening
Each method has its strengths, but integrating ligand-based and structure-based approaches enhances the accuracy of predictions, leading to better drug candidate identification.
One of the cutting-edge LBVS techniques that enhances QSAR modeling is Atomic Property Field (APF) Technology, developed by Molsoft. Let’s explore how APF can revolutionize molecular modeling and lead optimization.
Introduction to APF
APF (Atomic Property Field) technology is an advanced computational method that helps generate 3D QSAR models by encoding the spatial distribution of molecular properties. APF can be generated from one or more high-affinity scaffolds and seven properties are assigned from empiric physico-chemical components. These properties include: hydrogen bond donors, acceptors, Sp2 hybridization, lipophilicity, size, electropositive/negative, and charge (as shown in figure):
How APF Technology Works in Molsoft
Molsoft’s APF technology enhances the QSAR modeling process by using a grid-based approach to calculate atomic property fields at each grid point within a 3D space. The procedure includes several steps:
Molecular Representation: Molsoft generates a 3D model of the molecule using atomic coordinates and assigns specific atomic properties to each atom, such as partial charges or hydrophobicity.
Grid Generation: The 3D space around the molecule is divided into a grid, and at each point of the grid, the molecular properties are evaluated. This allows for the generation of a complete “field” that reflects the properties of the molecule in three dimensions.
Property Calculation: The atomic properties (like hydrogen bond donors, acceptors, Sp2 hybridization, lipophilicity, size, electropositive/negative and charge) are computed at every grid point, creating a “field” that represents the molecular characteristics in 3D space.
Feature Extraction for QSAR Modeling: Once the atomic property field is generated, the data can be used as input for QSAR models. The 3D distribution of molecular properties across the grid can be correlated with biological activity, helping researchers identify the molecular features most responsible for activity.
Benefits of APF Technology in Drug Discovery
Enhanced Accuracy in QSAR Models: The inclusion of 3D information leads to more accurate predictions of how a compound will interact with its biological target. By considering the spatial arrangement of atoms and their properties, APF captures subtle but significant interactions that 2D descriptors may miss.
Better Molecular Similarity Assessment: Traditional QSAR methods often struggle to assess the similarity of compounds accurately when only 2D descriptors are used. APF improves molecular comparison by considering 3D spatial features, making it easier to identify compounds with similar binding profiles or biological activities.
Identification of Key Interaction Points: APF technology can highlight critical regions in a molecule that influence its activity. This information is invaluable for optimizing lead compounds by focusing on modifying key areas of the molecular structure that interact directly with the target.
Improved Predictive Power: By encoding a wider range of molecular properties in three dimensions, APF helps create more robust predictive models. These models are less prone to errors and can provide better guidance during the drug design process, leading to faster development of effective drug candidates.
Scaffold-Hopping and Lead Optimization: APF can be used for scaffold-hopping, where researchers identify new scaffolds that retain similar bioactivity profiles. This can lead to the discovery of more diverse and effective lead compounds, which is particularly useful in the early stages of drug development.
Applications of APF Technology
Case study
Giganti et al. assessed the performance of eight different 3D virtual screening (3DVLS) programs across 11 DUD (Directory of Useful Decoys) systems, utilizing their respective DUD-own databases, which provide a more challenging and realistic benchmarking environment. The study found that all evaluated programs were capable of performing molecular alignments with standard parameters. However, ICM demonstrated the highest efficiency, benefiting from its probability-biased Monte Carlo approach, which effectively manages ligand flexibility. In terms of enrichment performance, which measures a program’s ability to distinguish active compounds from decoys, results were generally acceptable but varied depending on the target and the software used. Among the evaluated methods, Surflex-dock and ICM exhibited the best overall enrichment performance, as highlighted in the figure.
Reference:
- Totrov, Maxim. “Atomic property fields: generalized 3D pharmacophoric potential for automated ligand superposition, pharmacophore elucidation and 3D QSAR.” Chemical biology & drug design 71.1 (2008): 15-27.
- Totrov, Maxim. “Ligand binding site superposition and comparison based on Atomic Property Fields: identification of distant homologues, convergent evolution and PDB-wide clustering of binding sites.” BMC bioinformatics 12.Suppl 1 (2011): S35.
- Giganti, David, et al. “Comparative evaluation of 3D virtual ligand screening methods: impact of the molecular alignment on enrichment.” Journal of chemical information and modeling 50.6 (2010): 992-1004.