How can I model large molecules like macrocycles?
What comprises large molecules? When we talk about “large molecules,” we often think of biologics like monoclonal antibodies, proteins, and…
Optibrium demonstrates superior molecular docking method for small molecules and macrocycles
CAMBRIDGE, UK, 22 October 2024 – Optibrium, a leading developer of software and AI solutions for molecular design today announced…
Structure-based pose prediction: Non-cognate docking extended to macrocyclic ligands
In this paper, we describe an extended benchmark for non-cognate docking of macrocyclic ligands, and the superior performance of Surflex-Dock…
Molecular docking: Extrapolating to new scaffolds with Surflex-Dock
Interested in improving your binding mode predictions? Surflex-Dock is a unique method for molecular docking, offering automatic pipelines for ensemble docking, applicable to both small molecules and large peptidic macrocycles alike.
Optibrium demonstrates accelerated lead optimisation in complex agrochemical development
Optibrium’s QuanSA 3D-QSAR method uses an active learning approach to successfully and more efficiently identify a mimic of a macrocyclic natural product
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…
Macrocyclic lead optimisation
Macrocycles are becoming increasingly popular in drug discovery, due to their vast potential against previously “undruggable” targets. But the size…
From UK-2A to florylpicoxamid: active learning to identify a mimic of a macrocyclic natural product
Scaffold replacement as part of an optimisation process that requires maintenance of potency, desirable biodistribution, metabolic stability, and considerations of synthesis at very large scale is a complex challenge.
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…
Optibrium acquires BioPharmics LLC, expanding its 3D drug design and visualisation offering
BioPharmics’ Drs Ajay Jain (CEO) and Ann Cleves (Director of Applied Science) join the Optibrium team as Vice Presidents in the newly-created BioPharmics Division
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.
Unmasking the true identity of rapamycin’s minor conformer
The solution structure of the minor conformer of rapamycin was investigated using a combination of NMR techniques and computational methods
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.
A distributional model of bound ligand conformational strain: from small molecules to large peptidic macrocycles
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.
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.
