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Publications & Presentations


Predicting pKa Using a Combination of Quantum Mechanical and Machine Learning Methods

Monday, 18 November 2019 13:23
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Peter Hunt

Journal of Chemical Information and Modeling. Publication Date (Web):May 1, 2020

Peter Hunt1, Layla Hosseini-Gerami2, Tomas Chrien1, Jeffrey Plante3, David J. Ponting3, Matthew Segall1

1Optibrium Ltd. 2Department of Chemistry, Cambridge. 3Lhasa Ltd

Abstract

The acid dissociation constant (pKa) has an important influence on molecular properties crucial to compound development in synthesis, formulation and optimisation of absorption, distribution, metabolism and excretion properties. We will present a method that combines quantum mechanical calculations, at a semi-empirical level of theory, with machine learning to accurately predict pKa for a diverse range of mono- and polyprotic compounds. The resulting model has been tested on two external data sets, one specifically used to test pKa prediction methods (SAMPL6) and the second covering known drugs containing basic functionalities. Both sets were predicted with excellent accuracy (root-mean-square errors of 0.7 – 1.0 log units), comparable to other methodologies using much higher level of theory and computational cost.

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


Large Scale Imputation of Drug Discovery Data using Deep Learning

Wednesday, 27 May 2020 09:14
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Benedict Irwin

Held on 26 May 2020.

This webinar was presented by Ben Irwin and Matthew Segall (Optibrium), Tom Whitehead (Intellegens) and Scott Rowland (Takeda Pharmaceutical).

We presented a case study in which Alchemite was applied to a data set comprising approximately 700,000 compounds and 1,000 experimental endpoints. This explored a variety of applications, including imputation and prediction of compound activities in project contexts, high-throughput screening results and a diverse range of ADME properties.

Unlike conventional machine learning approaches, Alchemite learns directly from sparse and noisy experimental data across multiple endpoints, typical of those available in drug discovery. In combination with molecular descriptors, this enables it to learn directly both from correlations between experimental endpoints as well as structure-activity relationships to make better predictions. Furthermore, the model provides a robust estimate of the confidence in each prediction, enabling attention to be focused on only the most accurate results.

To hear the narration, please increase your speaker volume.


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Latest Forums


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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


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Find Similar Compounds in PubChem, ChEMBL or the PDB

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This script provides the ability to search PubChem, ChEMBL or the PDB for similar compounds.

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