ArticleFrontiers in pharmacology2020
Improvement of Prediction Performance With Conjoint Molecular Fingerprint in Deep Learning.
Article in Frontiers in pharmacology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
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- HP-MoleQ: An Effective Predictive Model for High-Throughput Screening of Food-Derived Hepatoprotective Compounds.Interdisciplinary sciences, computational life sciences · 2026Article
- Benchmarking the Ligand-HER2 Interactions Using Machine Learning and Molecular Dynamics Simulations.ACS omega · 2026Article
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- Computational prioritization of multi-target inhibitors: explainable QSAR and docking-based discovery of dual AChE/BACE1 chemotypes.Journal of computer-aided molecular design · 2026Article
- Machine learning reveals targets ofFrontiers in immunology · 2026Article
- Cross-Species Multitask Learning with Molecular and ADME Descriptors for Liver Microsomal Metabolic Stability.Computational and structural biotechnology journal · 2026Article
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- Enhancing cancer drug discovery: QSAR modeling with machine learning and chemical representations.PloS one · 2026Article
- Multimodal Cross-Attention Molecular Property Prediction for Text, Sequence, Graph, and Geometry.ACS omega · 2025Article
- Reverse engineering molecules from fingerprints through deterministic enumeration and generative models.Journal of cheminformatics · 2025Article
- Elucidating the Neuroprotective Mechanism of 5,7-Dimethoxyflavone and 5,7',4'-Trimethoxyflavone Through In Silico Target Prediction and in Memory-Impaired Mice.Neurochemical research · 2025Article
- Dual inhibition of AChE and MAO-B in Alzheimer's disease: machine learning approaches and model interpretations.Molecular diversity · 2025Article
- Retention order prediction of peptides containing non-proteinogenic amino acids.Bioinformatics advances · 2025Article
- Multimodal fused deep learning for drug property prediction: Integrating chemical language and molecular graph.Computational and structural biotechnology journal · 2024Article
- Towards the prediction of drug solubility in binary solvent mixtures at various temperatures using machine learning.Journal of cheminformatics · 2024Article
- A consensual machine-learning-assisted QSAR model for effective bioactivity prediction of xanthine oxidase inhibitors using molecular fingerprints.Molecular diversity · 2024Article
- Computational Insights into Reproductive Toxicity: Clustering, Mechanism Analysis, and Predictive Models.International journal of molecular sciences · 2024Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The accurate predicting of physical properties and bioactivity of drug molecules in deep learning depends on how molecules are represented. Many types of molecular descriptors have been developed for quantitative structure-activity/property relationships quantitative structure-activity relationships (QSPR). However, each molecular descriptor is optimized for a specific application with encoding preference. Considering that standalone featurization methods may only cover parts of information of the chemical molecules, we proposed to build the conjoint fingerprint by combining two supplementary fingerprints. The impact of conjoint fingerprint and each standalone fingerprint on predicting performance was systematically evaluated in predicting the logarithm of the partition coefficient (logP) and binding affinity of protein-ligand by using machine learning/deep learning (ML/DL) methods, including random forest (RF), support vector regression (SVR), extreme gradient boosting (XGBoost), long short-term memory network (LSTM), and deep neural network (DNN). The results demonstrated that the conjoint fingerprint yielded improved predictive performance, even outperforming the consensus model using two standalone fingerprints among four out of five examined methods. Given that the conjoint fingerprint scheme shows easy extensibility and high applicability, we expect that the proposed conjoint scheme would create new opportunities for continuously improving predictive performance of deep learning by harnessing the complementarity of various types of fingerprints.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.