GOLD
Protein–Ligand Docking Software: A Benchmark for Drug Discovery Accuracy and Flexibility
GOLD is a widely recognized protein–ligand docking software based on a genetic algorithm. It is known for its high accuracy in predicting ligand binding and flexibility in handling diverse drug discovery protein docking scenarios.
The software has a global reach and helps researchers identify and optimize potential drug candidates by predicting how small molecules, or ligands, interact with their target proteins.
It is renowned for its versatility in handling a wide range of docking scenarios. As a premier molecular docking engine, GOLD employs four scoring functions —ChemPLP, ChemScore, GoldScore, and ASP— along with various heuristics and advanced docking technologies to generate bioactive poses.
Benefits include covalent docking, ensemble docking, ligand flexibility search options, and protein side-chain flexibility using the Cambridge Structural Database (CSD) knowledge-based database.
GOLD accommodates functional waters, cofactors, metal ions, metal-ligand interactions, and a comprehensive set of user-defined constraints such as hydrogen bonds, distance, region, pharmacophore, interaction motif, similarity, substructure, scaffold, and soft potentials.
With a global user base, GOLD aids researchers in identifying and optimizing potential drug candidates by predicting the interactions between small molecules (ligands) and their target proteins.
Benefits of Using GOLD in Your Drug Discovery Research
Covalent Docking
Understand irreversible binding with covalent docking to explore cancer, immunology, and infectious disease targets.
Pose Prediction
Validate your ligand docking results and optimize hits to leads.
Highly Configurable Constraints
Use your existing knowledge of the system to bias results and focus on known features and behaviours.
Multiple Scoring Functions
Score and rescore to build a full picture of your system or perform consensus scoring.
Flexible Docking
Perform ensemble docking or handle flexible side-chains with soft potentials.
Water Handling
Assess how structural waters affect binding, and see if the ligand displaces waters or mediates the interaction during docking.
Virtual Screening
Unlimited potential with virtual screening powered by cloud or cluster (HPC).
Python API Access
Run dockings programmatically - for parameter optimization and workflow incorporation.
KNIME Component
Perform protein–ligand docking in the KNIME interface to easily build and create workflows.
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Use Cases
Introduction to Protein–Ligand Docking using GOLD
Questions from the Drug Discovery Community
Explore our complete list of FAQs in our knowledgebase.
FAQs
GOLD offers a range of fitness functions including GoldScore, ChemScore, ChemPLP, and ASP. ChemPLP is a good default to start with, but you can explore the different options depending on your system. See more in the user guide here.
Applying constraints allows you to bias the protein–ligand docking results to account for known features and behaviours.
GOLD allows distance constraints, hydrogen bond constraints, region (hydrophobic) constraints, pharmacophore constraints, similarity constraints, scaffold match constraint and interaction motif constraint to be applied during docking.
GOLD has been validated in the following peer-reviewed publications;
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- A new test set for validating predictions of protein-ligand interaction, Nissink et al., Proteins, https://doi.org/10.1002/prot.10232
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Improved protein–ligand docking using GOLD, Verdonk et al., Proteins, https://doi.org/10.1002/prot.10465
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Diverse, high-quality test set for the validation of protein-ligand docking performance, Hartshorn et al., J. Med. Chem., https://doi.org/10.1021/jm061277y
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Pose prediction and virtual screening performance of GOLD scoring functions in a standardized test, Liebeschuetz et al., J. Comput. Aided Mol. Des., https://doi.org/10.1007/s10822-012-9551-4
GOLD stands for Genetic Optimization for Ligand Docking. It is a software based on a genetic algorithm, for docking flexible ligands into protein binding sites.
No, however, we do have high-performance cluster (HPC) tools for virtual screening, and the Python API to write small-scale parallelization scripts. GOLD performs well in a single thread for standard tasks.
GOLD supports common input and output files including .mol (MDL sd), .mol2 (Tripos), .pdb, and .ent.