Enamine screening collection
StarDrop users who have licensed the Surflex eSim3D module can freely download prepared virtual screening collections for use in StarDrop. Enamine’s commercially available screening…
MolPort screening collection
StarDrop users who have licensed the Surflex eSim3D module can freely download prepared virtual screening collections for use in StarDrop. MolPort’s commercially available screening…
eMolecules screening collection
StarDrop users who have licensed the Surflex eSim3D module can freely download prepared virtual screening collections for use in StarDrop. eMolecules‘ commercially available screening…
Challenges and approaches in 3D ligand-based drug design
Ann and Ajay discuss the science behind and applications of the eSim molecular similarity method, a ligand-based drug design approach which considers surface-shape, electrostatics, and directionally sensitive hydrogen-bonding when comparing two molecules.
Metabolism prediction, 3D virtual screening and more – meet StarDrop 7.5
We explore the exciting new features in the latest release of StarDrop, built to elevate your drug discovery projects. These include the all-new Metabolism module; high performance virtual screening; additional workflow improvements
3D virtual screening with Surflex eSim3D
When exploring chemistry space around a known hit or lead, you can use 3D virtual screening to identify new compounds…
Complex peptide macrocycle optimisation: combining NMR restraints with conformational analysis to guide structure-based and ligand-based design
Systematic optimisation of large macrocyclic peptide ligands is a serious challenge. Here, we describe an approach for lead optimisation using the PD-1/PD-L1 system as a retrospective example of moving from initial lead compound to clinical candidate.
Integrated AI synthesis prediction in drug discovery
Learn more about how AI, machine learning and other computational tools can support the discovery process, bringing you feasible synthetic routes to your target compounds.
eSim3D: electrostatic-field and surface-shape similarity for ligand-based drug design
In this webinar, we present eSim3D, a novel ligand-based drug design approach based on electrostatic-field and surface-shape similarity coupled with unique conformational search capabilities, offering unprecedented accuracy and performance.
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.
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.
