How to perform fast and accurate 3D ligand-based affinity predictions
Binding affinity prediction continues to be a challenge in computer-aided drug design, especially in the absence of a high-quality target…
Computational and experimental integration in modern cancer drug discovery: The synthetic lethality approach
Join us for a webinar featuring experts from IDEAYA Biosciences and CDD Vault as they explore how synthetic lethality is…
How to build a better QSAR model
To guide drug design, it’s important to understand the likely ADME and physicochemical properties of your compounds at an early…
Explaining the ‘black box’: deep learning in drug discovery
Recent years have seen a remarkable rise in the number and scope of artificial intelligence and machine learning (especially deep…
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.
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.
Avoid the hype – how to successfully implement AI in drug discovery
AI has the potential to transform discovery. However, to ensure real impact, there are several practicalities that organisations must consider…
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?
An augmented approach to generative chemistry
Generative molecular design provides new exciting avenues of chemical space exploration. But how can we use these methods effectively to assess many optimisation strategies and find the compounds destined for success in our projects?
Join Dr Matt Segall and Dr Michael Parker as they explore state-of-the-art generative chemistry, and discuss the importance of an augmented intelligence approach for successful discovery.
Macrocyclic lead optimisation
Macrocycles are becoming increasingly popular in drug discovery, due to their vast potential against previously “undruggable” targets. But the size…
How to run a successful drug discovery team
Watch industry leaders from Novartis, Apollo Therapeutics and Eikon Therapeutics as they discuss their highs and lows, experience and advice,…
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…
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
