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New approaches to QSAR: Neural networks and machine learning
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/chin.198203201openalex-snapshot
ChemInform Abstract: 2,4‐DIAMINO‐5‐BENZYLPYRIMIDINES AND ANALOGS AS ANTIBACTERIAL AGENTS. 5. 3′,5′‐DIMETHOXY‐4′‐SUBSTITUTED‐BENZYL ANALOGS OF TRIMETHOPRIM
inferredcites →https://doi.org/10.1007/978-1-4899-3099-6_2openalex-snapshot
Statistical aspects of neural networks
inferredcites →https://doi.org/10.1007/978-1-4899-3324-9openalex-snapshot
Density Estimation for Statistics and Data Analysis
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A machine learning approach to computer-aided molecular design
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Artificial neural systems
inferredcites →https://doi.org/10.1021/ci00009a015openalex-snapshot
Neural networks as nonlinear structure-activity relationship analyzers. Useful functions of the partial derivative method in multilayer neural networks
inferredcites →https://doi.org/10.1021/ja00226a005openalex-snapshot
Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins
inferredcites →https://doi.org/10.1021/ja00856a042openalex-snapshot
Correlation analysis. Its application to the structure-activity relation of triazines inhibiting dihydrofolate reductase
inferredcites →https://doi.org/10.1021/jm00059a003openalex-snapshot
Applications of neural networks in structure-activity relationships of a small number of molecules
inferredcites →https://doi.org/10.1021/jm00095a016openalex-snapshot
Application of neural networks: quantitative structure-activity relationships of the derivatives of 2,4-diamino-5-(substituted-benzyl)pyrimidines as DHFR inhibitors
inferredcites →https://doi.org/10.1021/jm00113a022openalex-snapshot
Applications of neural networks in quantitative structure-activity relationships of dihydrofolate reductase inhibitors
inferredcites →https://doi.org/10.1021/jm00140a005openalex-snapshot
2,4-Diamino-5-benzylpyrimidines and analogs as antibacterial agents. 5. 3',5'-Dimethoxy-4'-substituted-benzyl analogs of trimethoprim
inferredcites →https://doi.org/10.1021/jm00165a004openalex-snapshot
Neural networks applied to structure-activity relationships
inferredcites →https://doi.org/10.1021/jm00171a037openalex-snapshot
Neural networks applied to pharmaceutical problems. III. Neural networks applied to quantitative structure-activity relationship (QSAR) analysis
inferredcites →https://doi.org/10.1021/jm00349a003openalex-snapshot
Comparison of the inhibition of Escherichia coli and Lactobacillus casei dihydrofolate reductase by 2,4-diamino-5-(substituted-benzyl)pyrimidines: quantitative structure-activity relationships, x-ray crystallography, and computer graphics in structure-activity analysis
inferredcites →https://doi.org/10.1021/jm00385a017openalex-snapshot
2,4-Diamino-5-benzylpyrimidines as antibacterial agents. 7. Analysis of the effect of 3,5-dialkyl substituent size and shape on binding to four different dihydrofolate reductase enzymes
inferredcites →https://doi.org/10.1038/194178b0openalex-snapshot
Correlation of Biological Activity of Phenoxyacetic Acids with Hammett Substituent Constants and Partition Coefficients
inferredcites →https://doi.org/10.1038/323533a0openalex-snapshot
Learning representations by back-propagating errors
inferredcites →https://doi.org/10.1073/pnas.89.23.11322openalex-snapshot
Drug design by machine learning: the use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase.
inferredcites →https://doi.org/10.1080/01621459.1981.10477729openalex-snapshot
Projection Pursuit Regression
inferredcites →https://doi.org/10.1093/comjnl/15.2.155openalex-snapshot
Numerical Initial Value Problems in Ordinary Differential Equations
inferredcites →https://doi.org/10.1093/oso/9780198538509.003.0013openalex-snapshot
A Comparative Study of Classification Algorithms: Statistical, Machine Learning and Neural Network
inferredcites →https://doi.org/10.1109/hicss.2001openalex-snapshot
Proceedings of the 34th Annual Hawaii International Conference on System Sciences
inferredcites →https://doi.org/10.2307/2005674openalex-snapshot
Numerical Initial Value Problems in Ordinary Differential Equations
inferredcites →https://doi.org/10.2307/2528164openalex-snapshot
A General Maximum Likelihood Discriminant
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/3527600906.mcb.200300066openalex-snapshot
Drug Bioavailability, Distribution and Clearance Prediction
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Developing Best Practices for Descriptor‐Based Property Prediction: Appropriate Matching of Datasets, Descriptors, Methods, and Expectations
inferred← citeshttps://doi.org/10.1002/jcc.27292openalex-snapshot
Machine learning‐assisted search for novel coagulants: When machine learning can be efficient even if data availability is low
inferred← citeshttps://doi.org/10.1002/psc.1345openalex-snapshot
Structure–activity relationship study of antioxidative peptides by QSAR modeling: the amino acid next to C‐terminus affects the activity
inferred← citeshttps://doi.org/10.1007/978-1-61779-965-5_9openalex-snapshot
Predictive Cheminformatics in Drug Discovery: Statistical Modeling for Analysis of Micro-array and Gene Expression Data
inferred← citeshttps://doi.org/10.1007/s10071-007-0129-9openalex-snapshot
Classification of dog barks: a machine learning approach
inferred← citeshttps://doi.org/10.1016/b978-0-12-374194-3.00023-8openalex-snapshot
Quantitative Approaches to Structure–Activity Relationships
inferred← citeshttps://doi.org/10.1016/b978-0-443-29808-0.00032-7openalex-snapshot
Applications of machine learning in quantitative structure-activity relationships (QSAR)
inferred← citeshttps://doi.org/10.1016/b978-012213815-7/50002-9openalex-snapshot
Strengths and Weaknesses of the Backpropagation Neural Network in QSAR and QSPR Studies
inferred← citeshttps://doi.org/10.1016/b978-012744481-9/50026-xopenalex-snapshot
QUANTITATIVE APPROACHES TO STRUCTURE–ACTIVITY RELATIONSHIPS
inferred← citeshttps://doi.org/10.1016/j.chemolab.2015.06.016openalex-snapshot
Chemometrics tools in QSAR/QSPR studies: A historical perspective
inferred← citeshttps://doi.org/10.1016/j.chemolab.2024.105278openalex-snapshot
A review of quantitative structure-activity relationship: The development and current status of data sets, molecular descriptors and mathematical models
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2019.01.008openalex-snapshot
In silico approaches and tools for the prediction of drug metabolism and fate: A review
inferred← citeshttps://doi.org/10.1016/j.jtbi.2012.10.029openalex-snapshot
Characterization of structure–antioxidant activity relationship of peptides in free radical systems using QSAR models: Key sequence positions and their amino acid properties
inferred← citeshttps://doi.org/10.1016/s0169-7439(97)00061-0openalex-snapshot
Introduction to multi-layer feed-forward neural networks
inferred← citeshttps://doi.org/10.1016/s1056-8719(00)00073-3openalex-snapshot
Use of recursive partitioning in the sequential screening of G-protein–coupled receptors
inferred← citeshttps://doi.org/10.1021/ci9003865openalex-snapshot
Ranking Chemical Structures for Drug Discovery: A New Machine Learning Approach
inferred← citeshttps://doi.org/10.1021/ci940128yopenalex-snapshot
A Neural Device for Searching Direct Correlations between Structures and Properties of Chemical Compounds
inferred← citeshttps://doi.org/10.1021/envhealth.3c00138openalex-snapshot
Predicting Bioaccumulation of Nanomaterials: Modeling Approaches with Challenges
inferred← citeshttps://doi.org/10.1021/jm0303195openalex-snapshot
Classification of Kinase Inhibitors Using a Bayesian Model
inferred← citeshttps://doi.org/10.1038/s41598-024-63708-2openalex-snapshot
Combined interaction of fungicides binary mixtures: experimental study and machine learning-driven QSAR modeling
inferred← citeshttps://doi.org/10.1039/d0ra05014hopenalex-snapshot
Predicting the emission wavelength of organic molecules using a combinatorial QSAR and machine learning approach
inferred← citeshttps://doi.org/10.1098/rstb.1994.0075openalex-snapshot
Application of machine learning to structural molecular biology
inferred← citeshttps://doi.org/10.1111/j.1399-3011.1994.tb01140.xopenalex-snapshot
Computational molecular modeling in peptide drug design
inferred← citeshttps://doi.org/10.1517/17460441.2016.1146250openalex-snapshot
Use of machine learning approaches for novel drug discovery
inferred← citeshttps://doi.org/10.3390/molecules18010735openalex-snapshot
Benchmarking Ligand-Based Virtual High-Throughput Screening with the PubChem Database
inferred← citeshttps://doi.org/10.3390/ph15050584openalex-snapshot
Virtual Screening Based on Machine Learning Explores Mangrove Natural Products as KRASG12C Inhibitors
inferred← citeshttps://doi.org/10.35537/10915/175500openalex-snapshot
Evaluación de harina de arvejas y productos derivados como ingredientes funcionales antioxidantes
inferred← citeshttps://doi.org/10.35537/10915/78721openalex-snapshot
Evaluación de aislado proteico de amaranto como fuente de péptidos antioxidantes: estudios in vitro e in vivo
inferred← citeshttps://doi.org/10.4018/978-1-7998-1204-3.ch011openalex-snapshot
Strategies of Virtual Screening in Medicinal Chemistry
inferred← citeshttps://doi.org/10.4018/ijqspr.2018010108openalex-snapshot
Strategies of Virtual Screening in Medicinal Chemistry
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