Studying and mitigating the effects of data drifts on ML model performance at the example of chemical toxicity data

Morger, Andrea; Lomana, Marina Garcia de; Norinder, Ulf; Svensson, Fredrik; Kirchmair, Johannes; Mathea, Miriam; Volkamer, Andrea

doi:10.1038/s41598-022-09309-3

Cited by 7 publications

(3 citation statements)

References 51 publications

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“…One of these BM scaffolds was exclusively found in the GRML library (Figure 13b), while the other was found in both the RML and GRML libraries (Figure 13c). This evidence not only confirms Molpher's efficacy in generating active GR scaffolds but also, considering the challenging benchmark imposed by the temporal split [74] , the rediscovery of two active compounds is a noteworthy achievement. Five distinct agonist/GR complexes were used to construct the model: dexamethasone (PDB ID: 1P93 [66] ), pyrazole-based agonist (PDB ID: 3E7C [67] ), indazole-based agonist (PDB ID: 4CSJ [68] ), pyrimidine-based agonist (PDB ID: 6EL7 [69] ), and next-gen indazole-based agonist (PDB ID: 7PRX [70] ).…”

Section: Prospective Validationsupporting

confidence: 62%

Chemical space exploration with Molpher: Generating and assessing a glucocorticoid receptor ligand library

Agea,

Čmelo,

Dehaen

et al. 2024

Preprint

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Computational exploration of chemical space is crucial in modern cheminformatics research for accelerating the discovery of new biologically active compounds. In this study, we present a detailed analysis of the chemical library of potential glucocorticoid receptor (GR) ligands generated by the molecular generator, Molpher. To generate the targeted GR library and to construct the classification models, structures from the ChEMBL database as well as from the internal IMG library, that was experimentally screened for biological activity in the primary luciferase reporter cell assay, were utilized. The composition of the targeted GR ligand library was compared with a reference library that randomly samples chemical space. A random forest model was used to determine the biological activity of ligands, incorporating its applicability domain using conformal prediction. It was demonstrated that the GR library is significantly enriched with GR ligands compared to the random library. Furthermore, prospective analysis demonstrated that Molpher successfully designed compounds which were subsequently experimentally confirmed to be active on the GR. A collection of 34 potential new GR ligands was also identified. Moreover, an important contribution of this study is the establishment of a comprehensive workflow for the evaluation of computationally generated ligands, particularly those with potential activity against targets that are challenging to dock.

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Section: Prospective Validationsupporting

confidence: 62%

Chemical space exploration with Molpher: Generating and assessing a glucocorticoid receptor ligand library

Agea,

Čmelo,

Dehaen

et al. 2024

Preprint

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“…Recently, AL has played an essential role in predicting the biological and physical activities of small molecules in the fields of biology and chemistry. This includes predicting the structure of proteins, − as well as the toxicity of compounds. , However, how does AL deal with data sets that have a small number of labeled elements? Numerous works have been proposed to address this issue, which are outlined below.…”

Section: Methods For Small Molecular Data Challengesmentioning

confidence: 99%

“…This includes predicting the structure of proteins, 436−438 as well as the toxicity of compounds. 439,440 However, how does AL deal with data sets that have a small number of labeled elements? Numerous works have been proposed to address this issue, which are outlined below.…”

Section: Active Learningmentioning

confidence: 99%

Machine Learning Methods for Small Data Challenges in Molecular Science

et al. 2023

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Small data are often used in scientific and engineering research due to the presence of various constraints, such as time, cost, ethics, privacy, security, and technical limitations in data acquisition. However, big data have been the focus for the past decade, small data and their challenges have received little attention, even though they are technically more severe in machine learning (ML) and deep learning (DL) studies. Overall, the small data challenge is often compounded by issues, such as data diversity, imputation, noise, imbalance, and high-dimensionality. Fortunately, the current big data era is characterized by technological breakthroughs in ML, DL, and artificial intelligence (AI), which enable data-driven scientific discovery, and many advanced ML and DL technologies developed for big data have inadvertently provided solutions for small data problems. As a result, significant progress has been made in ML and DL for small data challenges in the past decade. In this review, we summarize and analyze several emerging potential solutions to small data challenges in molecular science, including chemical and biological sciences. We review both basic machine learning algorithms, such as linear regression, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), kernel learning (KL), random forest (RF), and gradient boosting trees (GBT), and more advanced techniques, including artificial neural network (ANN), convolutional neural network (CNN), U-Net, graph neural network (GNN), Generative Adversarial Network (GAN), long short-term memory (LSTM), autoencoder, transformer, transfer learning, active learning, graph-based semi-supervised learning, combining deep learning with traditional machine learning, and physical model-based data augmentation. We also briefly discuss the latest advances in these methods. Finally, we conclude the survey with a discussion of promising trends in small data challenges in molecular science.

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