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Publications & Presentations


Practical Applications of Deep Learning to Impute Heterogeneous Drug Discovery Data

Wednesday, 29 April 2020 13:59
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Benedict Irwin

Preprint Paper.

Abstract

Contemporary deep learning approaches still struggle to bring a useful improvement in the field of drug discovery due to the challenges of sparse, noisy and heterogeneous data that are typically encountered in this context. We use a state-of-the-art deep learning method, Alchemite™, to impute data from drug discovery projects, including multi-target biochemical activities, phenotypic activities in cell-based assays, and a variety of absorption, distribution, metabolism, and excretion (ADME) endpoints. The resulting model gives excellent predictions for activity and ADME endpoints, offering an average increase in R2 of 0.22 versus quantitative structure-activity relationship methods. The model accuracy is robust to combining data across uncorrelated endpoints and projects with different chemical spaces, enabling a single model to be trained for all compounds and endpoints. We demonstrate improvements in accuracy on the latest chemistry and data when updating models with new data as an ongoing medicinal chemistry project progresses.

You can download this paper as a PDF
You can download the supplementary material as a PDF


AI-guided Design of Antimalarials with In Vitro Validation

Wednesday, 10 June 2020 09:14
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Benedict Irwin

Held on 26 May 2020.

AI-guided Design of Antimalarials with In Vitro Validation

This webinar was presented by Ben Irwin and Matthew Segall (Optibrium) and Professor Matthew Todd (University College London).

In this webinar you can hear how a combination of generative chemistry and deep learning has impacted the design of new compounds in the search for novel antimalarials.

We were joined by our guest Professor Matthew Todd, Chair of Drug Discovery at University College London and founder of the Open Source Malaria initiative, which aims to find new medicines for the treatment of malaria, recognised by the World Health Organisation as one of the world’s deadliest diseases.

We described how Optibrium’s team deployed the in silico generative chemistry capabilities of StarDrop™ and the cutting-edge AI technologies of Augmented Chemistry™ in the Open Source Malaria (OSM) initiative, to design active compounds against a novel target in Plasmodium falciparum.

To hear the narration, please increase your speaker volume.


Latest Forums


Number of Chiral Carbons

Thursday, 09 April 2020 10:39

I have downloaded the models for determining the number of carbons and number of heavy atoms, but does anyone have a process to calculate the number of chiral carbons? I am not looking to determine absolute stereochemistry, just determine if there is a chiral carbons present. Thank you, Chase


Category: General - How Do I...?
Views: 13820

ChEMBL and AtlasCBS synergies in StarDrop

Sunday, 05 April 2020 10:10

Colleagues, Please, find in the attached file a brief description and examples of the synergies between ChEMBL and AtlasCBS within the StarDrop environment. I will be happy to answer questions and do look forward to your feedback. Regards, Cele. https://www.optibrium.com/community/images/fbfiles/files/ChEMBL_and_AtlasCBS_StarDrop_posting.pdf


Category: Community
Views: 9612

Re:Trend line in Scatter Plot

Thursday, 20 February 2020 12:40

I'll answer myself: Visualization --> Customize Chart --> Lines


Category: General - How Do I...?
Views: 0

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GalaXi® Search Tool

Monday, 01 June 2020 12:13

The GalaXi® Search Tool is an add-on to StarDrop which searches the WuXi GalaXi® space for compounds that are similar to a query structure of interest. WuXi's GalaXi® is a chemical space that contains billions of virtual compounds, largely built from WuXi AppTec’s library of novel drug-like scaffolds. Due to a carefully selected building block supply and proven chemistry, the GalaXi® space consists of molecules that can be quickly synthesized on demand. Once you’ve identified compounds of interest, you can easily send the required...

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