Watch our panel of experts as they discuss tactics to achieve success in your medicinal chemistry projects, experiences they’ve had and advice they would give. Learn about the key challenges today’s drug hunter needs to overcome, the skills it takes to gain success across pharma and biotech, and what the future may hold for this industry.
We show that the distribution of expected global strain energy values is dependent on molecular size in a superlinear manner. The distribution of strain energy follows a rectified normal distribution whose mean and variance are related to conformational complexity.
From the manuscript “DOCKSTRING: Easy Molecular Docking Yields Better Benchmarks for Ligand Design”, Miguel García-Ortegón, Sergio Bacallado, et al¹ have developed…
In this example, using StarDrop’s R-group clipping tool, we will quickly transform chemical building blocks into their corresponding substituents, ready to enumerate a virtual library in StarDrop’s Nova module.
This worked example explores ways to assess and design compounds in 3D using the SeeSAR Pose module.
This worked example explores ways to assess the binding affinity of docked compounds.
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.
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.
Innovative predictive methods support virtual screening and compound design in the absence of 3D structure data.
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.
To better understand conformational propensities, global strain energies were estimated for 156 protein-macrocyclic peptide cocrystal structures.
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.
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.
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”).
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.
We introduce the QuanSA method for inducing physically meaningful field-based models of ligand binding pockets based on structure-activity data alone.
We introduce the ForceGen method for 3D structure generation and conformer elaboration of drug-like small molecules.
Surflex-QMOD integrates chemical structure and activity data to produce physically-realistic models for binding affinity prediction.
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.
