AutoDock Models for the StarDrop Pose Generation Interface (PGI)
From the manuscript “DOCKSTRING: Easy Molecular Docking Yields Better Benchmarks for Ligand Design”, Miguel García-Ortegón, Sergio Bacallado, et al¹ have developed…
Ligand-based drug design using Surflex eSim 3D
This worked example uses StarDrop’s Surflex eSim3D module to assess a small library of compounds for their similarity to known Heat Shock Protein 90 (HSP90) ligands. ideo archive.
MPO explorer: sensitivity analysis
In this example, we will use the Sensitivity Analysis tool in StarDrop’s MPO Explorer module to check if the ranking of compounds in a data set is sensitive to any of the criteria or importance values in a scoring profile.
MPO explorer: automatically building a scoring profile with rule induction
In this example we will use the Profile Builder in StarDrop’s MPO Explorer module to derive a multi-parameter scoring profile, based on a CNS data set.
Guided multi-parameter optimisation of 2D and 3D SAR
This SeeSAR and ADME QSAR worked example uses a combination of 2D and 3D methods to understand and optimise a virtual library of Heat Shock Protein 90 (HSP90) inhibitors.
Synergy and complementarity between focused machine learning and physics-based simulation in affinity prediction
We present results on the extent to which physics-based simulation (exemplified by FEP+) and focused machine learning (exemplified by QuanSA) are complementary for ligand affinity prediction.
Imputation of sensory properties using deep learning: webinar
In this webinar, we demonstrate how Augmented Chemistry®, a unique deep learning method, can learn from higher throughput data together with limited panel data to provide high-quality imputations for sensory properties.
Optibrium releases 3D ligand-based design module for StarDrop drug discovery software
Innovative predictive methods support virtual screening and compound design in the absence of 3D structure data.
Conformational strain of macrocyclic peptides in ligand–receptor complexes based on advanced refinement of bound-state conformers
To better understand conformational propensities, global strain energies were estimated for 156 protein-macrocyclic peptide cocrystal structures.
XGen: real-space fitting of complex ligand conformational ensembles to x-ray electron density maps
We report a new method for X-ray density ligand fitting and refinement that is suitable for a wide variety of small-molecule ligands, including macrocycles.
Structure- and ligand-based virtual screening on DUD-E+: performance dependence on approximations to the binding pocket
Using the DUD-E+ benchmark, we explore the impact of using a single protein pocket or ligand for virtual screening compared with using ensembles of alternative pockets, ligands, and sets thereof.
Electrostatic-field and surface-shape similarity for virtual screening and pose prediction
We introduce a new method for rapid computation of 3D molecular similarity that combines electrostatic field comparison with comparison of molecular surface-shape and directional hydrogen-bonding preferences (called “eSim”).
Complex macrocycle exploration: parallel, heuristic, and constraint-based conformer generation using ForceGen
ForceGen is both faster and more accurate than the best of all tested methods on a very large, independently curated benchmark of 2859 PDB ligands. In this study, the primary results are on macrocycles, including results for 431 unique examples from four separate benchmarks.
Quantitative surface field analysis: learning causal models to predict ligand binding affinity and pose
We introduce the QuanSA method for inducing physically meaningful field-based models of ligand binding pockets based on structure-activity data alone.
ForceGen 3D structure and conformer generation: from small lead-like molecules to macrocyclic drugs
We introduce the ForceGen method for 3D structure generation and conformer elaboration of drug-like small molecules.
Extrapolative prediction using physically-based QSAR
Surflex-QMOD integrates chemical structure and activity data to produce physically-realistic models for binding affinity prediction.
Knowledge-guided docking: accurate prospective prediction of bound configurations of novel ligands using Surflex-Dock
We present an approach that uses structural information known prior to a particular cutoff-date to make predictions on ligands whose bounds structures were determined later. The knowledge-guided docking protocol was tested on a set of ten protein targets using a total of 949 ligands.
Chemical and protein structural basis for biological crosstalk between PPARα and COX enzymes
Here we present an analysis of novel drug/target predictions, focusing on those that were not obvious based on known pharmacological crosstalk.
A structure-guided approach for protein pocket modeling and affinity prediction
We present a hybrid structure-guided strategy that combines molecular similarity, docking, and multiple-instance learning such that information from protein structures can be used to inform models of structure–activity relationships.
