Improving chemical similarity ensemble approach in target prediction

In the context of bioactivity prediction, the question of how to calibrate a score produced by a machine learning method into reliable probability of binding to a protein target is not yet satisfactorily addressed. In this study, we compared the performance of three such methods, namely Platt Scaling, Isotonic Regression and Venn-ABERS in calibrating prediction scores for ligand-target prediction comprising the Naïve Bayes, Support Vector Machines and Random Forest algorithms with bioactivity data available at AstraZeneca (40 million data points (compound-target pairs) across 2112 targets). Performance was assessed using Stratified Shuffle Split (SSS) and Leave 20% of Scaffolds Out (L20SO) validation.

Download Full-text

The Earth Mover’s Distance as a Metric for the Space of Inorganic Compositions

10.26434/chemrxiv.12777566.v1 ◽

2020 ◽

Author(s):

Cameron Hargreaves ◽

Matthew Dyer ◽

Michael Gaultois ◽

Vitaliy Kurlin ◽

Matthew J Rosseinsky

Keyword(s):

Euclidean Distance ◽

Nearest Neighbor ◽

Nearest Neighbor Search ◽

Inorganic Crystal Structure Database ◽

Earth Mover’S Distance ◽

Chemical Similarity ◽

Earth Mover's Distance ◽

Neighbor Search ◽

The Earth ◽

Binary Compounds

It is a core problem in any field to reliably tell how close two objects are to being the same, and once this relation has been established we can use this information to precisely quantify potential relationships, both analytically and with machine learning (ML). For inorganic solids, the chemical composition is a fundamental descriptor, which can be represented by assigning the ratio of each element in the material to a vector. These vectors are a convenient mathematical data structure for measuring similarity, but unfortunately, the standard metric (the Euclidean distance) gives little to no variance in the resultant distances between chemically dissimilar compositions. We present the Earth Mover’s Distance (EMD) for inorganic compositions, a well-defined metric which enables the measure of chemical similarity in an explainable fashion. We compute the EMD between two compositions from the ratio of each of the elements and the absolute distance between the elements on the modified Pettifor scale. This simple metric shows clear strength at distinguishing compounds and is efficient to compute in practice. The resultant distances have greater alignment with chemical understanding than the Euclidean distance, which is demonstrated on the binary compositions of the Inorganic Crystal Structure Database (ICSD). The EMD is a reliable numeric measure of chemical similarity that can be incorporated into automated workflows for a range of ML techniques. We have found that with no supervision the use of this metric gives a distinct partitioning of binary compounds into clear trends and families of chemical property, with future applications for nearest neighbor search queries in chemical database retrieval systems and supervised ML techniques.

Download Full-text

Epigenetic Target Prediction with Accurate Machine Learning Models

10.26434/chemrxiv.13522313 ◽

2021 ◽

Author(s):

Norberto Sánchez-Cruz ◽

Jose L. Medina-Franco

Keyword(s):

Machine Learning ◽

Small Molecules ◽

Predictive Models ◽

Large Scale ◽

Target Prediction ◽

Quantitative Measure ◽

Learning Models ◽

Discovery Research ◽

Drug Discovery Research ◽

Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

Download Full-text

Identification of BAP1-associated MicroRNAs and Implications in Cancer Development

10.31487/j.ijcst.2019.01.01 ◽

2019 ◽

pp. 1-4

Author(s):

Tikam Chand ◽

Tikam Chand

Keyword(s):

Gene Regulation ◽

In Silico ◽

Mirna Target ◽

Target Prediction ◽

Differential Regulation ◽

Cancer Development ◽

Mirna Target Prediction ◽

Cancer Types ◽

In Silico Tools

Having role in gene regulation and silencing, miRNAs have been implicated in development and progression of a number of diseases, including cancer. Herein, I present potential miRNAs associated with BAP1 gene identified using in-silico tools such as TargetScan and Exiqon miRNA Target Prediction. I identified fifteen highly conserved miRNA (hsa-miR-423-5p, hsa-miR-3184-5p, hsa-miR-4319, hsa-miR125b-5p, hsa-miR-125a-5p, hsa-miR-6893-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-505-3p.1, hsa-miR-429, hsa-miR-370-3p, hsa-miR-125a-5p, hsa-miR-141-3p, hsa-miR-200a-3p, and hsa-miR-429) associated with BAP1 gene. We also predicted the differential regulation of these twelve miRNAs in different cancer types.

Download Full-text