Artificial intelligence in drug development: Present status and future prospects

Artificial intelligence in healthcare: past, present and future

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Can the pharmaceutical industry reduce attrition rates?

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Principles of early drug discovery

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Artificial intelligence in medicine

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Diagnosing the decline in pharmaceutical R&D efficiency

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Planning chemical syntheses with deep neural networks and symbolic AI

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The rise of deep learning in drug discovery

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Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

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Deep Learning in Medical Imaging: General Overview

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Organs-on-chips at the frontiers of drug discovery

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DeepTox: toxicity prediction using deep learning

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The Drug Repurposing Hub: a next-generation drug library and information resource

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Automating drug discovery

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Wearable Devices in Medical Internet of Things: Scientific Research and Commercially Available Devices

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Applications of Deep Learning in Biomedicine

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Drug discovery and development: Role of basic biological research

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Deep learning improves prediction of drug–drug and drug–food interactions

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Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data

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druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico

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How artificial intelligence is changing drug discovery

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Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties

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Google DeepMind and healthcare in an age of algorithms

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Polypharmacology: drug discovery for the future

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Sample size calculations: basic principles and common pitfalls

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Computational approaches in target identification and drug discovery

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<i>De Novo</i> Design of Bioactive Small Molecules by Artificial Intelligence

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Chemical space as a source for new drugs

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Ace revisited: A new target for structure-based drug design

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Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships (RASAR) Outperforming Animal Test Reproducibility

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Using Artificial Intelligence to Reduce the Risk of Nonadherence in Patients on Anticoagulation Therapy

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Hit Identification and Optimization in Virtual Screening: Practical Recommendations Based on a Critical Literature Analysis

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A Data-Driven Approach to Predicting Successes and Failures of Clinical Trials

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Giving Drugs a Second Chance: Overcoming Regulatory and Financial Hurdles in Repurposing Approved Drugs As Cancer Therapeutics

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Deep into the Brain: Artificial Intelligence in Stroke Imaging

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How much do clinical trials cost?

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Emerging trends in geospatial artificial intelligence (geoAI): potential applications for environmental epidemiology

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Artificial Intelligence Approaches for Rational Drug Design and Discovery

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Drug Target Prediction and Repositioning Using an Integrated Network-Based Approach

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An application of machine learning to haematological diagnosis

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A multiple kernel learning algorithm for drug-target interaction prediction

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Model-based machine learning

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The rise of artificial intelligence and the uncertain future for physicians

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LigBuilder 2: A Practical de Novo Drug Design Approach

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Artificial intelligence in neurodegenerative disease research: use of IBM Watson to identify additional RNA-binding proteins altered in amyotrophic lateral sclerosis

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“Omics”-Informed Drug and Biomarker Discovery: Opportunities, Challenges and Future Perspectives

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Parsing clinical success rates

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Machine learning and computer vision approaches for phenotypic profiling

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Use of a Novel Artificial Intelligence Platform on Mobile Devices to Assess Dosing Compliance in a Phase 2 Clinical Trial in Subjects With Schizophrenia

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Cross sectional survey of multicentre clinical databases in the United Kingdom

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In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development

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In silico prediction of novel therapeutic targets using gene–disease association data

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The Cost of Drug Development

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Strategies to design clinical studies to identify predictive biomarkers in cancer research

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Machine intelligence decrypts β-lapachone as an allosteric 5-lipoxygenase inhibitor

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Unsupervised deep learning reveals prognostically relevant subtypes of glioblastoma

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A machine learning heuristic to identify biologically relevant and minimal biomarker panels from omics data

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An analysis of the influence of deep neural network (DNN) topology in bottleneck feature based language recognition

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Lead Generation - Enhancing the Success of Drug Discovery by Investing in the Hit to Lead Process

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Hot Spot Analysis for Driving the Development of Hits into Leads in Fragment-Based Drug Discovery

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Biotech R&D spend jumps by more than 15%

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Drug design for ever, from hype to hope

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Structure-Based Functional Design of Drugs: From Target to Lead Compound

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A new era of oncology through artificial intelligence

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ESP: A Method To Predict Toxicity and Pharmacological Properties of Chemicals Using Multiple MCASE Databases

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A novel framework for the identification of drug target proteins: Combining stacked auto-encoders with a biased support vector machine

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Exploring the Potential of Generative Adversarial Networks for Synthesizing Radiological Images of the Spine to be Used in In Silico Trials

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The rise and fall of machine learning methods in biomedical research

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Big Data in Pharmaceutical R&D: Creating a Sustainable R&D Engine

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Clinical Note Creation, Binning, and Artificial Intelligence

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Models for financing the regulation of pharmaceutical promotion

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The rise and fall of machine learning methods in biomedical research

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Data Mining for Biomedicine and Healthcare

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Computer-Aided Clinical Trial Recruitment Based on Domain-Specific Language Translation: A Case Study of Retinopathy of Prematurity

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Adapting drug discovery to Artificial Intelligence

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Why 90% of clinical drug development fails and how to improve it?

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Artificial intelligence in drug discovery and development

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Artificial intelligence to deep learning: machine intelligence approach for drug discovery

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Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

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A Review of the Role of Artificial Intelligence in Healthcare

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Applications of artificial intelligence and machine learning in smart cities

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A Structure-Based Drug Discovery Paradigm

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Challenges and advances in clinical applications of mesenchymal stromal cells

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The role of AI in drug discovery: challenges, opportunities, and strategies

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Advancing drug discovery via artificial intelligence

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Nanoparticles in Drug Delivery: From History to Therapeutic Applications

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Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions

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Adoption of artificial intelligence in smart cities: A comprehensive review

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Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development

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Artificial Intelligence in Healthcare and Medicine: Clinical Applications, Therapeutic Advances, and Future Perspectives

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Artificial intelligence for drug discovery: Resources, methods, and applications

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Leveraging Data Science to Combat COVID-19: A Comprehensive Review

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Organ-on-a-chip meets artificial intelligence in drug evaluation

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Artificial Intelligence in Pharmaceutical and Healthcare Research

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CADD, AI and ML in drug discovery: A comprehensive review

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In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery

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Generative artificial intelligence empowers digital twins in drug discovery and clinical trials

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Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling

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Artificial intelligence and machine learning in drug discovery and development

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Microfluidics for Drug Development: From Synthesis to Evaluation

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Application of Artificial Intelligence in COVID-19 drug repurposing

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Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review

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