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Deep imputation on large‐scale drug discovery data

https://doi.org/10.1002/ail2.31
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Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s10822-015-9855-2openalex-snapshot
The challenges of making decisions using uncertain data
inferredcites →https://doi.org/10.1016/j.commatsci.2018.02.002openalex-snapshot
Materials data validation and imputation with an artificial neural network
inferredcites →https://doi.org/10.1016/j.drudis.2018.05.010openalex-snapshot
Machine learning in chemoinformatics and drug discovery
inferredcites →https://doi.org/10.1016/j.drudis.2018.11.014openalex-snapshot
Artificial intelligence in drug development: present status and future prospects
inferredcites →https://doi.org/10.1016/j.fluid.2019.112259openalex-snapshot
Predicting physical properties of alkanes with neural networks
inferredcites →https://doi.org/10.1016/j.jhealeco.2016.01.012openalex-snapshot
Innovation in the pharmaceutical industry: New estimates of R&D costs
inferredcites →https://doi.org/10.1016/j.matdes.2017.06.007openalex-snapshot
Design of a nickel-base superalloy using a neural network
inferredcites →https://doi.org/10.1016/j.scriptamat.2017.11.008openalex-snapshot
Probabilistic design of a molybdenum-base alloy using a neural network
inferredcites →https://doi.org/10.1021/acs.jcim.0c00443openalex-snapshot
Practical Applications of Deep Learning To Impute Heterogeneous Drug Discovery Data
inferredcites →https://doi.org/10.1021/acs.jcim.0c00502openalex-snapshot
Uncertainty Quantification Using Neural Networks for Molecular Property Prediction
inferredcites →https://doi.org/10.1021/acs.jcim.7b00087openalex-snapshot
Demystifying Multitask Deep Neural Networks for Quantitative Structure–Activity Relationships
inferredcites →https://doi.org/10.1021/acs.jcim.7b00166openalex-snapshot
Profile-QSAR 2.0: Kinase Virtual Screening Accuracy Comparable to Four-Concentration IC50s for Realistically Novel Compounds
inferredcites →https://doi.org/10.1021/acs.jcim.8b00768openalex-snapshot
Imputation of Assay Bioactivity Data Using Deep Learning
inferredcites →https://doi.org/10.1021/ci100203eopenalex-snapshot
Compound Set Enrichment: A Novel Approach to Analysis of Primary HTS Data
inferredcites →https://doi.org/10.1023/a:1010933404324openalex-snapshot
Random Forests
inferredcites →https://doi.org/10.1038/nrd3078openalex-snapshot
How to improve R&D productivity: the pharmaceutical industry's grand challenge
inferredcites →https://doi.org/10.1038/s42256-020-0156-7openalex-snapshot
Predicting the state of charge and health of batteries using data-driven machine learning
inferredcites →https://doi.org/10.1063/5.0004377openalex-snapshot
Enhancing NEMD with automatic shear rate sampling to model viscosity and correction of systematic errors in modeling density: Application to linear and light branched alkanes
inferredcites →https://doi.org/10.1093/nar/gkt1031openalex-snapshot
The ChEMBL bioactivity database: an update
inferredcites →https://doi.org/10.1109/mlsp.2017.8168143openalex-snapshot
Macau: Scalable Bayesian factorization with high-dimensional side information using MCMC
inferredcites →https://doi.org/10.17863/cam.56834openalex-snapshot
Enhancing NEMD with automatic shear rate sampling to model viscosity and correction of systematic errors in modeling density: Application to linear and light branched alkanes.
inferredcites →https://doi.org/10.21236/ada486804openalex-snapshot
Relational Learning via Collective Matrix Factorization
inferredcites →https://doi.org/10.2174/138161212799436430openalex-snapshot
Multi-Parameter Optimization: Identifying High Quality Compounds with a Balance of Properties
inferredcites →https://doi.org/10.3390/molecules23102520openalex-snapshot
Artificial Intelligence in Drug Design
inferredcites →https://doi.org/10.4155/fdd-2020-0008openalex-snapshot
Imputation Versus Prediction: Applications in Machine Learning for Drug Discovery
inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot
Scikit-learn: Machine Learning in Python

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-1-0716-3441-7_16openalex-snapshot
Applications of Big Data and AI-Driven Technologies in CADD (Computer-Aided Drug Design)
inferred← citeshttps://doi.org/10.1007/s10822-021-00424-3openalex-snapshot
Imputation of sensory properties using deep learning
inferred← citeshttps://doi.org/10.1007/s10822-026-00761-1openalex-snapshot
Comparing massively-multitask regression algorithms for drug discovery
inferred← citeshttps://doi.org/10.1016/b978-0-443-15862-9.00003-5openalex-snapshot
Lead Modification
inferred← citeshttps://doi.org/10.1016/j.drudis.2021.11.023openalex-snapshot
Enhancing preclinical drug discovery with artificial intelligence
inferred← citeshttps://doi.org/10.1039/d5dd00546aopenalex-snapshot
Generalized molecular property imputation using a flexible transformer architecture
inferred← citeshttps://doi.org/10.1042/bst20211240openalex-snapshot
Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD)
inferred← citeshttps://doi.org/10.1089/genbio.2024.0011openalex-snapshot
Neural Network Potentials for Enabling Advanced Small-Molecule Drug Discovery and Generative Design
inferred← citeshttps://doi.org/10.1186/s13040-024-00375-zopenalex-snapshot
Modelling the nicotine pharmacokinetic profile for e-cigarettes using real time monitoring of consumers’ physiological measurements and mouth level exposure
inferred← citeshttps://doi.org/10.3389/fbinf.2023.1121591openalex-snapshot
Perspective on the challenges and opportunities of accelerating drug discovery with artificial intelligence
inferred← citeshttps://doi.org/10.6084/m9.figshare.26322714openalex-snapshot
Additional file 1 of Modelling the nicotine pharmacokinetic profile for e-cigarettes using real time monitoring of consumers’ physiological measurements and mouth level exposure
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