Gaussian processes for classification
In this article, Olga describes how we extend the application of Gaussian Processes technique to classification problems. These computational techniques…
Beyond profiling: using ADMET models to guide decisions
Summary The main use of ADMET models, whether in silico or in vitro,tends to be molecule ‘profiling’; identifying compounds which are expected to…
How do I assess similarities between molecules?
What is the similarity principle? The similarity principle is one of the key concepts in drug design. It implies that…
How can I predict toxicity in drug discovery?
The importance of predicting toxicity early in drug development Highlighting safety and toxicity early in the drug development process is…
Why don’t my QSAR models work?
Why QSAR models matter in drug discovery Before we start troubleshooting, let’s remember why we build and run these models…
Leveraging structure-based and ligand-based techniques in drug discovery
In our recent article in Innovations in Pharmaceutical Technology, we explore the principles, techniques, and applications of structure-based and ligand-based…
Why should medicinal chemists use software?
What exactly is a medicinal chemist’s role today? Firstly, let’s define a medicinal chemist’s role – to design and synthesise…
Predicting selective herbicide activity with machine learning
The agrochemical industry is facing growing challenges around resistance, stringent regulations, and pressures to reduce the time and cost of…
Rapid AI generation of optimised compound designs, guided by user interaction
Pairing AI with human expertise We present a novel AI compound optimisation system, designed to include human oversight as a…
Macrocyclic peptide optimisation: Integrating computational approaches with biophysical data
Introduction Using the integrated set of computational methods within the BioPharmics™ Platform, macrocycles can be effectively modelled for lead optimisation.…
AI is not magic: A scientist’s guide to cutting through the hype
Ground truth matters more than algorithm hype In drug discovery, we deal in imperfect data. Assays are noisy. Endpoints are…
Combining deep learning with multi-parameter optimisation to predict compounds with selective activity against a broadleaf weed species
Introduction The emergence of resistance and increased stringency of regulatory requirements have created a need for new agrochemicals. The long…
Does AI remove bias in drug discovery?
Like all humans, drug discovery scientists suffer from inherent biases that influence our decision making. Our intuition can sometimes be…
How does Optibrium protect customer data?
The age of data The internet has transformed the way the world does business. As hardware and software have evolved,…
Open-source drug discovery software: It’s free, right?
When are open-source drug discovery solutions a good fit? We should first clarify that when we’re speaking about open-source tools,…
Machine Learning 101: How to optimise hyperparameters
What are parameters in machine learning models? The regular (non-hyper) parameters of an ML model are the numbers that it…
How do StarDrop and LiveDesign compare?
No glove fits every hand perfectly—and it’s the same with software tools. There’s no one-size-fits-all solution when it comes to…
How much does 3D molecular modelling software cost?
Introduction 3D molecular modelling plays a vital role in modern drug discovery, offering powerful applications to streamline research, reduce costs,…
What are the best AI drug discovery software platforms?
What value does AI offer in drug discovery? The potential is huge: To learn more about the value we’re seeing…
