Drug Development and Discovery Considering Artificial Intelligence: A Through Analysis

α
Dr. Sadia Parveen
Dr. Sadia Parveen
σ
Sachin Verma
Sachin Verma
ρ
Ridam Gaud
Ridam Gaud
Ѡ
Ankita Bhattacharjee
Ankita Bhattacharjee
α Jaipur National University

Send Message

To: Author

Drug Development and Discovery Considering Artificial Intelligence: A Through Analysis

Article Fingerprint

ReserarchID

PDDTM3JAQ0

Drug Development and Discovery Considering Artificial Intelligence: A Through Analysis Banner

AI TAKEAWAY

Connecting with the Eternal Ground
  • English
  • Afrikaans
  • Albanian
  • Amharic
  • Arabic
  • Armenian
  • Azerbaijani
  • Basque
  • Belarusian
  • Bengali
  • Bosnian
  • Bulgarian
  • Catalan
  • Cebuano
  • Chichewa
  • Chinese (Simplified)
  • Chinese (Traditional)
  • Corsican
  • Croatian
  • Czech
  • Danish
  • Dutch
  • Esperanto
  • Estonian
  • Filipino
  • Finnish
  • French
  • Frisian
  • Galician
  • Georgian
  • German
  • Greek
  • Gujarati
  • Haitian Creole
  • Hausa
  • Hawaiian
  • Hebrew
  • Hindi
  • Hmong
  • Hungarian
  • Icelandic
  • Igbo
  • Indonesian
  • Irish
  • Italian
  • Japanese
  • Javanese
  • Kannada
  • Kazakh
  • Khmer
  • Korean
  • Kurdish (Kurmanji)
  • Kyrgyz
  • Lao
  • Latin
  • Latvian
  • Lithuanian
  • Luxembourgish
  • Macedonian
  • Malagasy
  • Malay
  • Malayalam
  • Maltese
  • Maori
  • Marathi
  • Mongolian
  • Myanmar (Burmese)
  • Nepali
  • Norwegian
  • Pashto
  • Persian
  • Polish
  • Portuguese
  • Punjabi
  • Romanian
  • Russian
  • Samoan
  • Scots Gaelic
  • Serbian
  • Sesotho
  • Shona
  • Sindhi
  • Sinhala
  • Slovak
  • Slovenian
  • Somali
  • Spanish
  • Sundanese
  • Swahili
  • Swedish
  • Tajik
  • Tamil
  • Telugu
  • Thai
  • Turkish
  • Ukrainian
  • Urdu
  • Uzbek
  • Vietnamese
  • Welsh
  • Xhosa
  • Yiddish
  • Yoruba
  • Zulu

References

72 Cites in Article
  1. Indhupriya Subramanian,Srikant Verma,Shiva Kumar,Abhay Jere,Krishanpal Anamika (2020). Multi-omics Data Integration, Interpretation, and Its Application.
  2. Christof Angermueller,Tanel Pärnamaa,Leopold Parts,Oliver Stegle (2016). Deep learning for computational biology.
  3. X Zeng,X Song,T Ma,X Pan,Y Zhou,Y Hou,Z Zhang (2020). DeepTarget: End-to-end learning framework for predicting protein targets of small molecules.
  4. A Zhavoronkov,Q Vanhaelen,T Oprea (2020). Will Artificial Intelligence for Drug Discovery Impact Clinical Pharmacology?.
  5. D Szklarczyk,A Gable,D Lyon,A Junge,S Wyder,J Huerta-Cepas (2019). STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets.
  6. C Stark,B Breitkreutz,T Reguly,L Boucher,A Breitkreutz,M Tyers (2006). BioGRID: a general repository for interaction datasets.
  7. I Beltagy,K Lo,A Cohan SciBERT: A Pretrained Language Model for Scientific Text.
  8. D Ferrucci (2012). Introduction to "This is Watson.
  9. John Jumper,Richard Evans,Alexander Pritzel,Tim Green,Michael Figurnov,Olaf Ronneberger,Kathryn Tunyasuvunakool,Russ Bates,Augustin Žídek,Anna Potapenko,Alex Bridgland,Clemens Meyer,Simon Kohl,Andrew Ballard,Andrew Cowie,Bernardino Romera-Paredes,Stanislav Nikolov,Rishub Jain,Jonas Adler,Trevor Back,Stig Petersen,David Reiman,Ellen Clancy,Michal Zielinski,Martin Steinegger,Michalina Pacholska,Tamas Berghammer,Sebastian Bodenstein,David Silver,Oriol Vinyals,Andrew Senior,Koray Kavukcuoglu,Pushmeet Kohli,Demis Hassabis (2021). Highly accurate protein structure prediction with AlphaFold.
  10. A Aliper,S Plis,A Artemov,A Ulloa,P Mamoshina,A Zhavoronkov (2016). Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data.
  11. I Wallach,M Dzamba,A Heifets Atom Net: A deep convolutional neural network for bioactivity prediction in structure-based drug discovery.
  12. B Ramsundar,P Eastman,P Walters,V Pande (2019). Deep Learning for the Life Sciences.
  13. K Yang,K Swanson,Jin Coley,C Eiden,P Gao,H (2019). Analyzing Learned Molecular Representations for Property Prediction.
  14. A Zhavoronkov,Y Ivanenkov,A Aliper (2019). Deep learning enables rapid identification of potent DDR1 kinase inhibitors.
  15. J Jiménez,M Skalic,G Martinez-Rosell,De Fabritiis,G (2018). KDEEP: Protein-ligand absolute binding affinity prediction via 3D-convolutional neural networks.
  16. Z Liu,Y Li,L Han,J Li,J Liu,Z Zhao (2015). PDB-wide collection of binding data: current status of the PDBbind database.
  17. P Richardson,I Griffin,C Tucker,D Smith,O Oechsle,A Phelan (2020). Baricitinib as potential treatment for 2019-nCoV acute respiratory disease.
  18. Andreas Mayr,Günter Klambauer,Thomas Unterthiner,Sepp Hochreiter (2016). DeepTox: Toxicity Prediction using Deep Learning.
  19. P Banerjee,A Eckert,A Schrey,R Preissner (2018). ProTox-II: A webserver for the prediction of toxicity of chemicals.
  20. Douglas Pires,Tom Blundell,David Ascher (2015). pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures.
  21. B Zhang,A Korolj,Bfl Lai,M Radisic (2018). Advances in organ-on-a-chip engineering.
  22. Jae Ryu,Hyun Kim,Sang Lee (2018). Deep learning improves prediction of drug–drug and drug–food interactions.
  23. C Ross,I Swetlitz (2018). Watson Recommends Incorrect Cancer Treatments, System Training Questioned.
  24. D Bhatt,C Mehta (2016). Adaptive Designs for Clinical Trials.
  25. P Shah,F Kendall,S Khozin,R Goosen,J Hu,J Laramie (2019). Artificial intelligence and machine learning in clinical development: a translational perspective.
  26. A Montalbano,C Toumazou (2020). Wearable sensors for real-time clinical trial monitoring: current trends and future opportunities.
  27. A Beam,I Kohane (2018). Big data and machine learning in health care.
  28. A Holzinger,C Biemann,C Pattichis,D Kell (2017). What do we need to build explainable AI systems for the medical domain?.
  29. (2021). Artificial Intelligence and Machine Learning in Software as a Medical Device.
  30. Milena Gianfrancesco,Suzanne Tamang,Jinoos Yazdany,Gabriela Schmajuk (2018). Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data.
  31. W Price,I Cohen (2019). Privacy in the age of medical big data.
  32. E Topol (2019). High-performance medicine: the convergence of human and artificial intelligence.
  33. Doug Meil (2025). IBM Watson Health.
  34. (2021). Artificial Intelligence/Machine Learning-Based Software as a Medical Device (SaMD) Action Plan.
  35. Ema (2020). European Medicines Agency (EMA), The.
  36. Insilico Medicine (2021). AI-designed drug enters clinical trials.
  37. Sarfaraz Niazi,Zamara Mariam (2020). Artificial intelligence in drug development: reshaping the therapeutic landscape.
  38. J Nelson,B Smith,J Jared,J Younger (2011). Prospective trial of real-time electronic surveillance to expedite early care of severe sepsis.
  39. C Tan,H Chen,C Xia (2008). The prediction of cardiovascular disease based on trace element contents in hair and a classifier of boosting decision stumps.
  40. H Chen,C Tan (2011). Prediction of Type-2 diabetes based on several element levels in blood and chemometrics.
  41. Michael Hooper,Lisa Weavind,Arthur Wheeler,Jason Martin,Supriya Gowda,Matthew Semler,Rachel Hayes,Daniel Albert,Norment Deane,Hui Nian,Janos Mathe,Andras Nadas,Janos Sztipanovits,Anne Miller,Gordon Bernard,Todd Rice (2012). Randomized trial of automated, electronic monitoring to facilitate early detection of sepsis in the intensive care unit*.
  42. H Giannini,J Ginestra,C Chivers (2019). A machine learning algorithm to predict severe sepsis and septic shock: development, implementation, and impact on clinical practice.
  43. S Naushad,Janaki Ramaiah,M Pavithrakumari,M (2016). Artificial neural network-based exploration of gene-nutrient interactions in folate and xeno-biotic metabolic pathways that modulate susceptibility to breast cancer.
  44. J Vamathevan,D Clark,P Czodrowski (2019). Applications of machine learning in drug discovery and development.
  45. Erik Gawehn,Jan Hiss,Gisbert Schneider (2016). Deep Learning in Drug Discovery.
  46. Rafael Gómez-Bombarelli,Jennifer Wei,David Duvenaud,José Hernández-Lobato,Benjamín Sánchez-Lengeling,Dennis Sheberla,Jorge Aguilera-Iparraguirre,Timothy Hirzel,Ryan Adams,Alán Aspuru-Guzik (2018). Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules.
  47. Gisbert Schneider (2018). Automating drug discovery.
  48. R Miotto,L Li,B Kidd,J Dudley (2016). Deep Patient: an unsupervised representation to predict the future of patients from the electronic health records.
  49. A Bate,M Lindquist,I Edwards (1998). A Bayesian neural network method for adverse drug reaction signal generation.
  50. Elena Izmailova,John Wagner,Eric Perakslis (2018). Wearable Devices in Clinical Trials: Hype and Hypothesis.
  51. Y Wang,D Zeng,L Wang (2018). Social media analytics for drug safety signal detection.
  52. Rave Harpaz,Alison Callahan,Suzanne Tamang,Yen Low,David Odgers,Sam Finlayson,Kenneth Jung,Paea Lependu,Nigam Shah (2014). Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art.
  53. T Botsis,M Nguyen,E Woo (2011). Text mining for the biopharmaceutical domain: a systematic review of recent advances.
  54. H Luo,J Wang,M Li (2016). Drug repositioning based on comprehensive similarity measures and birandom walk algorithm.
  55. C Carvalho (2021). Functional genomics and CRISPR screens in drug discovery: Synergy with AI.
  56. J Lee (2020). BioBERT: A pre-trained biomedical language representation model for biomedical text mining.
  57. Yadi Zhou,Yuan Hou,Jiayu Shen,Yin Huang,William Martin,Feixiong Cheng (2020). Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2.
  58. H Chen,O Engkvist,Y Wang,M Olivecrona,T Blaschke (2018). The rise of deep learning in drug discovery.
  59. Sean Ekins,Ana Puhl,Kimberley Zorn,Thomas Lane,Daniel Russo,Jennifer Klein,Anthony Hickey,Alex Clark (2019). Exploiting machine learning for end-to-end drug discovery and development.
  60. J Stokes,K Yang,K Swanson (2020). A deep learning approach to antibiotic discovery.
  61. Jessica Vamathevan,Dominic Clark,Paul Czodrowski,Ian Dunham,Edgardo Ferran,George Lee,Bin Li,Anant Madabhushi,Parantu Shah,Michaela Spitzer,Shanrong Zhao (2019). Applications of machine learning in drug discovery and development.
  62. Han Altae-Tran,Bharath Ramsundar,Aneesh Pappu,Vijay Pande (2017). Low Data Drug Discovery with One-Shot Learning.
  63. W Walters,R Barzilay (2021). Applications of deep learning in molecule generation and molecular property prediction.
  64. A Zhavoronkov (2018). Artificial intelligence for drug discovery, biomarker development, and generation of novel chemistry.
  65. M Ragoza,J Hochuli,E Idrobo,J Sunseri,D Koes (2017). Protein-ligand scoring with convolutional neural networks.
  66. M Kuenemann,S Schmidl,A Korotcov (2021). Evaluation of graph neural networks for predicting drug-target interactions.
  67. L Chen,A Cruz,S Ramsey (2020). The rise of generative models in drug design.
  68. Q Liu,H Wu,X Wang (2021). Application of Artificial Intelligence in the Diagnosis and Treatment of COVID-19 Lung Disease.
  69. Junshui Ma,Robert Sheridan,Andy Liaw,George Dahl,Vladimir Svetnik (2015). Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships.
  70. D Elton,Z Boukouvalas,M Fuge,P Chung (2019). Deep learning for molecular design-a review of the state of the art.
  71. J Gilmer,S Schoenholz,P Riley,O Vinyals,G Dahl (2017). Neural message passing for quantum chemistry.
  72. Philippe Schwaller,Théophile Gaudin,Dávid Lányi,Costas Bekas,Teodoro Laino (2018). “Found in Translation”: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Dr. Sadia Parveen. 2026. \u201cDrug Development and Discovery Considering Artificial Intelligence: A Through Analysis\u201d. Global Journal of Medical Research - B: Pharma, Drug Discovery, Toxicology & Medicine GJMR-B Volume 25 (GJMR Volume 25 Issue B1): .

Download Citation

Journal Specifications

Crossref Journal DOI 10.17406/gjmra

Print ISSN 0975-5888

e-ISSN 2249-4618

Keywords
Version of record

v1.2

Issue date

November 6, 2025

Language
en
Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

Article Matrices
Total Views: 316
Total Downloads: 88
2026 Trends
Related Research

Published Article

Our website is actively being updated, and changes may occur frequently. Please clear your browser cache if needed. For feedback or error reporting, please email [email protected]

Request Access

Please fill out the form below to request access to this research paper. Your request will be reviewed by the editorial or author team.
X

Quote and Order Details

Contact Person

Invoice Address

Notes or Comments

This is the heading

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.

High-quality academic research articles on global topics and journals.

Drug Development and Discovery Considering Artificial Intelligence: A Through Analysis

Dr. Sadia Parveen
Dr. Sadia Parveen Jaipur National University
Sachin Verma
Sachin Verma
Ridam Gaud
Ridam Gaud
Ankita Bhattacharjee
Ankita Bhattacharjee

Research Journals