Which is the best metabolite prediction software?
Discover which metabolite prediction software is best for your needs in this comprehensive guide from Optibrium. Compare top tools like Meteor Nexus, MetaSite, and StarDrop to make informed decisions for drug metabolism prediction
What are the advantages of cloud-based drug discovery software?
We recently published a case study with Amazon Web Services, detailing how we were able to scale our StarDrop platform…
26th North American ISSX and JSSX Meeting
The joint ISSX/JSSX meeting is for researchers looking to gain a deeper understanding of drug metabolism and pharmacokinetics.
38th ACS National Medicinal Chemistry Symposium
Join Optibrium’s Chris Khoury at the 38th NMCS meeting in Seattle, 23-26 June
Physical Parameter Estimation vs. Pure Machine-Learning for Drug Design
Nearly all computational methods in the CADD field depend on parameters whose values are derived from various types of experimental…
Mastering multi-parameter optimisation
Develop advanced MPO strategies and target the right compounds, faster.
We’re diving back into our favourite subject: multi-parameter optimisation.
The complexity of collaboration in drug discovery
Everyone knows smooth collaboration can speed up successful drug discovery projects. But how can we collaborate easily in drug discovery…
Finding balance in drug discovery through multi-parameter optimisation
Successful drugs require a delicate balance of many properties, such as potency, ADME and toxicity, to meet a project’s therapeutic objective. To make decisions about compound progression and assay selection, the available data must be assessed against project-specific criteria. However, the data on which we base our decisions often come from different sources and can vary in quality, so how can we use this information to make confident decisions? In addition, how can we be sure that the criteria we’re using are the most appropriate?
Overcoming challenges in drug metabolism: in silico approaches
Interpreting metabolite-ID experiments; determining the right species for animal studies; providing optimisation suggestions for your medicinal chemistry colleagues to overcome…
Transferable machine learning interatomic potential for bond dissociation energy prediction of drug-like molecules
Predicting metabolism at an early stage is important in maximising the chance of a drug’s success. However, accurate, useful models…
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.
How to be a great drug discovery chemist
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.
Predicting regioselectivity of cytosolic SULT metabolism for drugs
This paper describes a model to predict whether a particular site on a molecule will be metabolised by cytosolic sulfotransferase enzymes (SULTs).
Integrated prediction of Phase I and II metabolism
Watch Optibrium CEO Matt Segall and Principal Scientist Mario Öeren as they explore groundbreaking new quantum mechanics and machine learning models which go beyond P450s and provide insights on a broad range of enzymes involved in drug metabolism.
Predicting reactivity to drug metabolism: beyond CYPs
Introduction Predicting sites of metabolism (SoM) enable chemists to be more efficient in optimising the structure of new chemical entities…
Virtual screening: Challenges, considerations and approaches for successful screens
Virtual screening presents a host of challenges, especially where little or no structural information on targets is available. So how can we best set our screening strategies up for success?
AI in early drug discovery: from promise to practice
In this webinar, Jeff Blaney (Senior Director of Discovery Chemistry, Genentech), Darren Green (Head of Cheminformatics & Data Science, GlaxoSmithKline), Julian Levell (Head of Discovery, New Equilibrium Biosciences), Matthew Segall (CEO, Optibrium) discuss the state of AI in early drug discovery from hit to preclinical candidate and share their experiences with and expectations of AI, including predictive modelling, synthesis prediction, and generative chemistry. Hear about the successes of AI drug discovery and an outlook on what AI needs to achieve to really transform the industry.
Prediction of in vivo pharmacokinetic parameters and time – exposure curves in rats using machine learning from the chemical structure
This article is a collaboration with Intellegens, the University of Cambridge and AstraZeneca. It provides a proof-of-concept study in which Cerella™ is used to predict rat in vivo pharmacokinetic (PK) parameters and concentration–time PK profiles.
