The challenges of making decisions using uncertain data
This peer-reviewed paper discusses the challenges of using uncertain experimental data to make confident decisions on the selection of compounds.…
Breaking free from chemical spreadsheets
This article explores the benefits of a more intuitive and flexible approach to viewing and interacting with drug discovery data,…
Advances in multi-parameter optimisation methods for de novo drug design
Summary This review article discusses recent developments in the methods and opinions around multi-parameter optimisation, focusing on applications to de novo drug…
Finding the rules for successful drug optimisation
Summary In this drug optimisation article, co-authored with Pfizer we discuss new ‘rule induction’ methods. These explore complex data to…
Addressing toxicity risk when designing and selecting compounds in early drug discovery
Summary In this article, ‘Addressing toxicity risk when designing and selecting compounds in early drug discovery‘, we discuss the application…
Preprint: considering the impact of ‘drug-like’ properties on the chance of success
Summary There are many different definitions of ‘drug-like’, with rules proposed based on property trends seen in successful drugs. In…
Iterative refinement of a binding pocket model: active computational steering of lead optimisation
Computational approaches for binding affinity prediction are most frequently demonstrated through cross-validation within a series of molecules or through performance shown on a blinded test set. Here, we show how such a system performs in an iterative, temporal lead optimization exercise. A series of gyrase inhibitors with known synthetic order formed the set of molecules that could be selected for “synthesis.”
Applying med chem transformations and multi-parameter optimisation
Summary This article on applying med chem transformations and multi-parameter optimisation describes the concepts and algorithms underlying StarDrop’s Nova module. We’ve developed…
Multi-parameter optimisation review article
In this multi-parameter optimisation review, we survey the range of methods used for MPO in drug discovery, compare their strengths…
Overcoming psychological barriers to good discovery decisions
Summary This article explores the psychological barriers and risks of cognitive biases to R&D decision-making. It contrasts current practice with…
Gaussian processes for classification: QSAR modeling of ADMET and target activity
Summary In this study, the researchers look to solve classification quantitative structure−activity relationship (QSAR) modelling problems using Gaussian processes. They…
ADMET property prediction: The state of the art and current challenges
Summary This article discusses Quantitative Structure – Activity relationships (QSAR) methods to predict absorption, distribution, metabolism, excretion and toxicity (ADMET)…
Automatic QSAR modeling of ADME properties: blood-brain barrier penetration and aqueous solubility
Summary In this study, our researchers combined an automatic model generation process for building QSAR models with the Gaussian Processes…
Compound prioritisation and selection
In this demo we’re going to take a look at how StarDrop can guide the prioritisation and selections of compounds using a combination of in vitro and in silico data.
Making priors a priority
Summary This article discusses a critical issue that the community needs to address address in order to use the predictive…
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
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
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.
