On the public citation graph
Integrated deep learned transcriptomic and structure-based predictor of clinical trials outcomes
0 signed · 83 inferred · view as graph
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.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/s10237-005-0072-0openalex-snapshot
An anatomically based patient-specific finite element model of patella articulation: towards a diagnostic tool
inferredcites →https://doi.org/10.1016/j.chembiol.2016.07.023openalex-snapshot
A Data-Driven Approach to Predicting Successes and Failures of Clinical Trials
inferredcites →https://doi.org/10.1016/j.clbc.2013.10.016openalex-snapshot
Randomized Phase II Trial of the Cyclin-Dependent Kinase Inhibitor Dinaciclib (MK-7965) Versus Capecitabine in Patients With Advanced Breast Cancer
inferredcites →https://doi.org/10.1016/j.drudis.2015.09.007openalex-snapshot
Why and how have drug discovery strategies in pharma changed? What are the new mindsets?
inferredcites →https://doi.org/10.1016/j.drudis.2016.09.019openalex-snapshot
Design of efficient computational workflows for in silico drug repurposing
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.lungcan.2013.11.020openalex-snapshot
Randomized phase 2 study of the cyclin-dependent kinase inhibitor dinaciclib (MK-7965) versus erlotinib in patients with non-small cell lung cancer
inferredcites →https://doi.org/10.1016/s0169-409x(00)00129-0openalex-snapshot
Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings 1PII of original article: S0169-409X(96)00423-1. The article was originally published in Advanced Drug Delivery Reviews 23 (1997) 3–25. 1
inferredcites →https://doi.org/10.1016/s1361-8415(02)00108-1openalex-snapshot
Patient specific finite element model of the face soft tissues for computer-assisted maxillofacial surgery
inferredcites →https://doi.org/10.1021/acs.chemrestox.5b00388openalex-snapshot
Current and Future Perspectives on the Development, Evaluation, and Application of in Silico Approaches for Predicting Toxicity
inferredcites →https://doi.org/10.1021/acs.molpharmaceut.5b00982openalex-snapshot
Applications of Deep Learning in Biomedicine
inferredcites →https://doi.org/10.1021/acs.molpharmaceut.6b00248openalex-snapshot
Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data
inferredcites →https://doi.org/10.1021/cc9800071openalex-snapshot
A Knowledge-Based Approach in Designing Combinatorial or Medicinal Chemistry Libraries for Drug Discovery. 1. A Qualitative and Quantitative Characterization of Known Drug Databases
inferredcites →https://doi.org/10.1021/jm020017nopenalex-snapshot
Molecular Properties That Influence the Oral Bioavailability of Drug Candidates
inferredcites →https://doi.org/10.1038/477526aopenalex-snapshot
Translational research: 4 ways to fix the clinical trial
inferredcites →https://doi.org/10.1038/nbt.2786openalex-snapshot
Clinical development success rates for investigational drugs
inferredcites →https://doi.org/10.1038/nchem.1243openalex-snapshot
Quantifying the chemical beauty of drugs
inferredcites →https://doi.org/10.1038/ncomms13427openalex-snapshot
In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development
inferredcites →https://doi.org/10.1038/nrclinonc.2011.165openalex-snapshot
Adaptive clinical trials in oncology
inferredcites →https://doi.org/10.1038/nrclinonc.2012.22openalex-snapshot
Drug development and clinical trials—the path to an approved cancer drug
inferredcites →https://doi.org/10.1038/nrd1468openalex-snapshot
Drug repositioning: identifying and developing new uses for existing drugs
inferredcites →https://doi.org/10.1038/nrd2445openalex-snapshot
The influence of drug-like concepts on decision-making in medicinal chemistry
inferredcites →https://doi.org/10.1038/nrd3681openalex-snapshot
Diagnosing the decline in pharmaceutical R&D efficiency
inferredcites →https://doi.org/10.1093/nar/27.1.29openalex-snapshot
KEGG: Kyoto Encyclopedia of Genes and Genomes
inferredcites →https://doi.org/10.1093/nar/28.1.27openalex-snapshot
KEGG: Kyoto Encyclopedia of Genes and Genomes
inferredcites →https://doi.org/10.1093/nar/gkn653openalex-snapshot
PID: The Pathway Interaction Database
inferredcites →https://doi.org/10.1093/nar/gkv1075openalex-snapshot
The SIDER database of drugs and side effects
inferredcites →https://doi.org/10.1097/01.ccm.0000142394.28791.c3openalex-snapshot
In silico design of clinical trials: A method coming of age
inferredcites →https://doi.org/10.1126/science.1132939openalex-snapshot
The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease
inferredcites →https://doi.org/10.1126/scitranslmed.3002273openalex-snapshot
How to Revive Breakthrough Innovation in the Pharmaceutical Industry
inferredcites →https://doi.org/10.1158/1078-0432.ccr-15-0624openalex-snapshot
A Phase I/II Multicenter, Open-Label Study of the Oral Histone Deacetylase Inhibitor Abexinostat in Relapsed/Refractory Lymphoma
inferredcites →https://doi.org/10.1186/1758-2946-3-18openalex-snapshot
AMBIT RESTful web services: an implementation of the OpenTox application programming interface
inferredcites →https://doi.org/10.12688/f1000research.10010.1openalex-snapshot
The TrialsTracker: Automated ongoing monitoring of failure to share clinical trial results by all major companies and research institutions
inferredcites →https://doi.org/10.1371/journal.pone.0002789openalex-snapshot
A Patient-Specific in silico Model of Inflammation and Healing Tested in Acute Vocal Fold Injury
inferredcites →https://doi.org/10.18203/2349-3259.ijct20161408openalex-snapshot
In silico clinical trials: how computer simulation will transform the biomedical industry
inferredcites →https://doi.org/10.18632/aging.100968openalex-snapshot
Deep biomarkers of human aging: Application of deep neural networks to biomarker development
inferredcites →https://doi.org/10.18632/aging.101034openalex-snapshot
Deep biomarkers of aging are population-dependent
inferredcites →https://doi.org/10.18632/oncotarget.14073openalex-snapshot
The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology
inferredcites →https://doi.org/10.2165/00002018-199920020-00002openalex-snapshot
The Medical Dictionary for Regulatory Activities (MedDRA)
inferredcites →https://doi.org/10.4155/cli.12.133openalex-snapshot
Rethinking Phase II clinical trial design in heart failure
inferredcites →https://doi.org/10.4230/oasics.dx.2026.15openalex-snapshot
AutoResearch Lite: a reproducible paper-inspired experiment runner (Adam mechanism reproduction and convergence-speed comparison)
inferredcites →https://doi.org/10.48550/arxiv.1212.5701openalex-snapshot
ADADELTA: An Adaptive Learning Rate Method
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/cphc.202200255openalex-snapshot
An Ensemble Structure and Physicochemical (SPOC) Descriptor for Machine‐Learning Prediction of Chemical Reaction and Molecular Properties
inferred← citeshttps://doi.org/10.1002/cpt.1795openalex-snapshot
Will Artificial Intelligence for Drug Discovery Impact Clinical Pharmacology?
inferred← citeshttps://doi.org/10.1002/cpt.3008openalex-snapshot
Prediction of Clinical Trials Outcomes Based on Target Choice and Clinical Trial Design with Multi‐Modal Artificial Intelligence
inferred← citeshttps://doi.org/10.1002/cpt.650openalex-snapshot
Artificial intelligence (AI) systems for interpreting complex medical datasets
inferred← citeshttps://doi.org/10.1007/978-3-030-24970-0_18openalex-snapshot
Deep Integrated Biomarkers of Aging
inferred← citeshttps://doi.org/10.1007/978-3-031-22792-9_13openalex-snapshot
Novel Methodology for Improving the Generalization Capability of Chemo-Informatics Deep Learning Models
inferred← citeshttps://doi.org/10.1007/978-3-031-93254-0_12openalex-snapshot
Enhancing Molecular Property Prediction with Dual-Level Representation Learning
inferred← citeshttps://doi.org/10.1007/978-3-031-98022-0_15openalex-snapshot
Contrastive Learning Approaches for Drug Discovery
inferred← citeshttps://doi.org/10.1007/978-981-96-8176-1_13openalex-snapshot
Artificial Intelligence in Oncology
inferred← citeshttps://doi.org/10.1007/978-981-97-0316-6_4openalex-snapshot
MolBench: A Benchmark of AI Models for Molecular Property Prediction
inferred← citeshttps://doi.org/10.1007/s11030-023-10771-yopenalex-snapshot
Deep learning algorithms applied to computational chemistry
inferred← citeshttps://doi.org/10.1007/s12312-019-00734-6openalex-snapshot
Künstliche Intelligenz – Anwendungsbereiche in der Onkologie
inferred← citeshttps://doi.org/10.1016/b978-0-12-824477-7.00007-9openalex-snapshot
AI applications in prevalent diseases and disorders
inferred← citeshttps://doi.org/10.1016/j.patter.2022.100445openalex-snapshot
HINT: Hierarchical interaction network for clinical-trial-outcome predictions
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c01862openalex-snapshot
Automatic Prediction of Molecular Properties Using Substructure Vector Embeddings within a Feature Selection Workflow
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.5c03159openalex-snapshot
A New Era of Artificial Intelligence (AI): Transforming Drug Discovery and Development
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.7b00346openalex-snapshot
druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.8b00110openalex-snapshot
Prediction of Human Cytochrome P450 Inhibition Using a Multitask Deep Autoencoder Neural Network
inferred← citeshttps://doi.org/10.1038/s41591-024-03434-4openalex-snapshot
Artificial intelligence in drug development
inferred← citeshttps://doi.org/10.1038/s41591-025-03743-2openalex-snapshot
A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial
inferred← citeshttps://doi.org/10.1038/s42004-025-01592-1openalex-snapshot
Molecular property prediction in the ultra‐low data regime
inferred← citeshttps://doi.org/10.1038/s44360-026-00081-6openalex-snapshot
A large-scale database for clinical trial outcomes and features
inferred← citeshttps://doi.org/10.1080/17460441.2023.2153830openalex-snapshot
Machine learning-based prediction of drug approvals using molecular, physicochemical, clinical trial, and patent-related features
inferred← citeshttps://doi.org/10.1093/bib/bbad423openalex-snapshot
MESPool: Molecular Edge Shrinkage Pooling for hierarchical molecular representation learning and property prediction
inferred← citeshttps://doi.org/10.1098/rsif.2017.0387openalex-snapshot
Opportunities and obstacles for deep learning in biology and medicine
inferred← citeshttps://doi.org/10.1101/2020.02.05.20020636openalex-snapshot
Predicting successes and failures of clinical trials with an ensemble LS-SVR
inferred← citeshttps://doi.org/10.1186/s13321-025-01073-6openalex-snapshot
FusionCLM: enhanced molecular property prediction via knowledge fusion of chemical language models
inferred← citeshttps://doi.org/10.1371/journal.pone.0226461openalex-snapshot
Predicting cancer origins with a DNA methylation-based deep neural network model
inferred← citeshttps://doi.org/10.18632/aging.102475openalex-snapshot
Deep biomarkers of aging and longevity: from research to applications
inferred← citeshttps://doi.org/10.21203/rs.3.rs-2724591/v1openalex-snapshot
Current status of artificial intelligence in cancer management: emerging trends, limitations and future outlook
inferred← citeshttps://doi.org/10.2144/fsoa-2021-0074openalex-snapshot
An Overview of Artificial Intelligence in Oncology
inferred← citeshttps://doi.org/10.2174/9789815136807123010009openalex-snapshot
Artificial Intelligence (AI) Game Changer in Cancer Biology
inferred← citeshttps://doi.org/10.22159/ijap.2025v17i5.54474openalex-snapshot
TECH-DRIVEN TRUST: THE ROLE OF AI AND EMERGING TECHNOLOGIES IN PHARMACEUTICAL QUALITY ASSURANCE
inferred← citeshttps://doi.org/10.26686/wgtn.25044362openalex-snapshot
De novo Molecular Design using Deep Learning
inferred← citeshttps://doi.org/10.3390/molecules31173008openalex-snapshot
MSGRL: A Motif-Driven Self-Supervised Graph Representation Learning Framework for Interpretable Molecular Property Prediction
inferred← citeshttps://doi.org/10.4155/fmc-2021-0243openalex-snapshot
Machine Learning and Deep Learning in Data-Driven Decision Making of Drug Discovery and Challenges in High-Quality Data Acquisition in the Pharmaceutical Industry
inferred← citeshttps://doi.org/10.5772/intechopen.84148openalex-snapshot
Designing Data-Driven Learning Algorithms: A Necessity to Ensure Effective Post-Genomic Medicine and Biomedical Research
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.