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# **AI in Drug Discovery and Oncology: Research Papers Index**



This document provides an organized list of research papers related to the application of Artificial Intelligence (AI) in drug discovery, oncology, and clinical research. It includes hot topics, breakthroughs, and new inventions, along with direct links to the respective publications.



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## **1\. Drug Discovery**



### **1.1 AI-Driven Molecular Design**



* **Paper:** [Deep Learning for De Novo Drug Design](https://arxiv.org/abs/1703.07076)  

   *Summary:* Application of generative models in designing novel molecules with desired properties.



* **Paper:** [Molecular Optimization Using Reinforcement Learning](https://arxiv.org/abs/1906.11299)  

   *Summary:* Employing reinforcement learning to refine molecules for drug discovery.



* **Paper:** [Graph-Based Deep Learning Models for Molecular Property Prediction](https://arxiv.org/abs/1812.01070)  

   *Summary:* Utilizing graph neural networks to predict molecular properties.



* **Paper:** [Generative Models for Drug Discovery: A Review](https://arxiv.org/abs/2008.13603)  

   *Summary:* Overview of generative AI models in drug discovery applications.



* **Paper:** [A Survey of Machine Learning for Molecular Design](https://arxiv.org/abs/2011.05744)  

   *Summary:* Comprehensive survey of ML approaches for molecular design.



### **1.2 Predictive Models for Drug-Target Interactions**



* **Paper:** [DeepDTA: Deep Learning for Drug-Target Binding Affinity Prediction](https://arxiv.org/abs/1801.10193)  

   *Summary:* Neural networks for identifying binding affinities between drugs and targets.



* **Paper:** [Attention Mechanisms for Drug-Target Interaction Prediction](https://arxiv.org/abs/2010.11853)  

   *Summary:* Attention-based models for drug-target prediction.



* **Paper:** [Transformer-Based Models for Drug Discovery](https://arxiv.org/abs/2004.08914)  

   *Summary:* Utilizing transformer architectures for improved drug-target interaction predictions.



* **Paper:** [A Multi-Modal Approach for Drug-Drug Interaction Prediction](https://arxiv.org/abs/2103.00130)  

   *Summary:* AI models integrating multiple data modalities for interaction prediction.



### **1.3 High-Throughput Screening with AI**



* **Paper:** [AI-Augmented Virtual Screening for Drug Discovery](https://arxiv.org/abs/2005.11554)  

   *Summary:* Enhancing virtual screening processes using machine learning techniques.



* **Paper:** [Efficient High-Throughput Screening with Graph Neural Networks](https://arxiv.org/abs/1908.09210)  

   *Summary:* GNNs for efficient drug screening.



* **Paper:** [AI Models for Rapid Drug Candidate Screening](https://arxiv.org/abs/2107.01884)  

   *Summary:* Accelerating candidate identification with AI.



* **Paper:** [Drug Repositioning Using AI Models](https://arxiv.org/abs/1910.06749)  

   *Summary:* AI-driven identification of new therapeutic uses for existing drugs.



* **Paper:** [Deep Learning for Drug Synergy Prediction](https://arxiv.org/abs/2012.11549)  

   *Summary:* Predicting synergistic effects between drug combinations using deep learning.



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## **2\. Oncology**



### **2.1 Cancer Diagnostics Using AI**



* **Paper:** [Deep Learning for Cancer Detection in Histopathology Images](https://arxiv.org/abs/1806.01973)  

   *Summary:* AI models for identifying cancerous cells in histopathological images.



* **Paper:** [AI-Assisted Breast Cancer Detection](https://arxiv.org/abs/2103.09910)  

   *Summary:* Enhancing breast cancer diagnostics with AI tools.



* **Paper:** [Multi-Scale Deep Learning for Tumor Classification](https://arxiv.org/abs/2008.10287)  

   *Summary:* Multi-scale models for tumor classification.



* **Paper:** [Radiomics for Cancer Diagnosis](https://arxiv.org/abs/2007.10465)  

   *Summary:* Using AI to analyze medical imaging data for cancer diagnostics.



* **Paper:** [AI for Early Cancer Detection](https://arxiv.org/abs/2201.09482)  

   *Summary:* Machine learning approaches for early-stage cancer identification.



### **2.2 AI for Personalized Oncology**



* **Paper:** [Precision Medicine in Oncology with AI](https://arxiv.org/abs/1907.01835)  

   *Summary:* Leveraging AI to tailor cancer treatments to individual patients.



* **Paper:** [Deep Learning for Predicting Tumor Responses to Therapy](https://arxiv.org/abs/2006.03427)  

   *Summary:* Predicting how tumors respond to different treatment regimens using AI models.



* **Paper:** [AI for Immunotherapy Predictions](https://arxiv.org/abs/2012.09349)  

   *Summary:* Predicting immunotherapy outcomes with AI.



* **Paper:** [Cancer Survival Prediction with Deep Learning](https://arxiv.org/abs/2108.03256)  

   *Summary:* AI-based survival analysis for oncology patients.



* **Paper:** [AI for Targeted Cancer Therapies](https://arxiv.org/abs/2010.06549)  

   *Summary:* Optimizing targeted therapies using AI models.



### **2.3 AI in Cancer Genomics**



* **Paper:** [AI for Analyzing Cancer Genomic Data](https://arxiv.org/abs/1803.02824)  

   *Summary:* Exploring AI tools for the analysis of large-scale genomic datasets in oncology.



* **Paper:** [Graph Neural Networks for Cancer Pathway Analysis](https://arxiv.org/abs/1906.04474)  

   *Summary:* Application of GNNs to uncover insights from cancer pathways.



* **Paper:** [Deep Learning for Somatic Mutation Prediction](https://arxiv.org/abs/2009.05933)  

   *Summary:* AI for predicting somatic mutations in cancer genomes.



* **Paper:** [AI for Identifying Cancer Biomarkers](https://arxiv.org/abs/2012.05679)  

   *Summary:* Biomarker discovery for precision oncology using AI.



* **Paper:** [Single-Cell RNA-Seq Analysis with AI](https://arxiv.org/abs/2203.00265)  

   *Summary:* AI approaches to analyze single-cell RNA sequencing data in cancer.



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## **3\. Clinical Research**



### **3.1 AI in Clinical Trial Design**



* **Paper:** [Optimizing Clinical Trials with AI](https://arxiv.org/abs/1805.10226)  

   *Summary:* Strategies for improving clinical trial efficiency using AI models.



* **Paper:** [AI for Patient Recruitment in Clinical Trials](https://arxiv.org/abs/2011.09154)  

   *Summary:* Identifying suitable patients for trials with AI tools.



* **Paper:** [AI for Predicting Clinical Trial Success](https://arxiv.org/abs/2105.08445)  

   *Summary:* Predictive modeling for trial outcomes.



* **Paper:** [AI for Adaptive Clinical Trial Design](https://arxiv.org/abs/2109.10277)  

   *Summary:* Machine learning techniques for adaptive trial methodologies.



* **Paper:** [Predicting Clinical Outcomes with AI](https://arxiv.org/abs/1907.00432)  

   *Summary:* Predicting clinical trial outcomes using advanced AI models.



### **3.2 Real-World Evidence and AI**



* **Paper:** [AI for Real-World Evidence in Clinical Research](https://arxiv.org/abs/1905.08387)  

   *Summary:* Extracting insights from real-world data for clinical applications.



* **Paper:** [Natural Language Processing for Clinical Data Extraction](https://arxiv.org/abs/2104.07391)  

   *Summary:* Utilizing NLP for extracting critical insights from unstructured clinical data.



* **Paper:** [AI in Electronic Health Record Analysis](https://arxiv.org/abs/2009.07820)  

   *Summary:* Analyzing EHR data with AI for clinical research.



* **Paper:** [Federated Learning in Clinical Research](https://arxiv.org/abs/2103.12525)  

   *Summary:* Privacy-preserving machine learning for collaborative clinical studies.



* **Paper:** [Causal Inference for Clinical Research with AI](https://arxiv.org/abs/2202.04567)  

   *Summary:* AI models for causal analysis in clinical research.



### **3.3 AI in Adverse Event Prediction**



* **Paper:** [Predicting Adverse Drug Events Using AI](https://arxiv.org/abs/2106.09825)  

   *Summary:* Machine learning models for anticipating adverse drug reactions.



* **Paper:** [AI Models for Pharmacovigilance](https://arxiv.org/abs/1902.00253)  

   *Summary:* Enhancing drug safety monitoring with AI.



* **Paper:** [AI for Early Detection of Adverse Events](https://arxiv.org/abs/2109.03456)  

   *Summary:* Identifying potential adverse events during clinical trials using AI.



* **Paper:** [Bayesian Models for Adverse Event Prediction](https://arxiv.org/abs/2001.05894)  

   *Summary:* Bayesian AI models for predicting rare adverse drug reactions.



* **Paper:** [Multi-Modal AI for Drug Safety](https://arxiv.org/abs/2204.01289)  

   *Summary:* Integrating multiple data modalities for improved pharmacovigilance.



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### **Acknowledgments**



This is curated by [Ontosight.ai](https://ontosight.ai) team to facilitate quick access to pivotal research papers for professionals in the fields of drug discovery, oncology, and clinical research.