QSAR Modeling: Where have you been? Where are you going to?

Cited by (1,001)

Items 301–400 of 1,001

1. Multi-Descriptor Read Across (MuDRA): A Simple and Transparent Approach for Developing Accurate Quantitative Structure–Activity Relationship Models
   Vinícius M. Alves, Alexander Golbraikh, Stephen J. Capuzzi, Kammy Liu, Wai In Lam, Daniel Korn, Diane Pozefsky, Carolina Horta Andrade, Eugene N. Muratov, Alexander Tropsha · 2018 · Journal of Chemical Information and Modeling
2. A machine learning workflow for molecular analysis: application to melting points
   Ganesh Sivaraman, Nicholas E. Jackson, Benjamín Sánchez-Lengeling, Álvaro Vázquez‐Mayagoitia, Alán Aspuru‐Guzik, Venkatram Vishwanath, Juan Pablo · 2020 · Machine Learning Science and Technology
3. MLSolvA: solvation free energy prediction from pairwise atomistic interactions by machine learning
   Hyuntae Lim, YounJoon Jung · 2021 · Journal of Cheminformatics
4. Machine Learning on DNA-Encoded Library Count Data Using an Uncertainty-Aware Probabilistic Loss Function
   Katherine S. Lim, Andrew G. Reidenbach, Bruce K. Hua, Jeremy W. Mason, Christopher J. Gerry, Paul A. Clemons, Connor W. Coley · 2022 · Journal of Chemical Information and Modeling
5. Relationships Between Aquatic Toxicity, Chemical Hydrophobicity, and Mode of Action: Log Kow Revisited
   Faith N. Lambert, Deborah N. Vivian, Sandy Raimondo, Caroline L. Tebes-Stevens, Mace G. Barron · 2022 · Archives of Environmental Contamination and Toxicology
6. Serverless Prediction of Peptide Properties with Recurrent Neural Networks
   Mehrad Ansari, Andrew D. White · 2023 · Journal of Chemical Information and Modeling
7. Algorithm selection for protein–ligand docking: strategies and analysis on ACE
   Tianlai Chen, Xiwen Shu, Huiyuan Zhou, Floyd A. Beckford, Mustafa Mısır · 2023 · Scientific Reports
8. Reliable method for predicting the binding affinity of RNA-small molecule interactions using machine learning
   Sowmya Ramaswamy Krishnan, Arijit Roy, M. Michael Gromiha · 2024 · Briefings in Bioinformatics
9. In SilicoPharmacoepidemiologic Evaluation of Drug-Induced Cardiovascular Complications Using Combined Classifiers
   Chuipu Cai, Jiansong Fang, Pengfei Guo, Qi Wang, Huixiao Hong, Javid J. Moslehi, Feixiong Cheng · 2018 · Journal of Chemical Information and Modeling
10. High-Throughput Screening to Predict Chemical-Assay Interference
    Alexandre Borrel, Ruili Huang, Srilatha Sakamuru, Menghang Xia, Anton M. Simeonov, Kamel Mansouri, Keith A. Houck, Richard Judson, Nicole Kleinstreuer · 2020 · Scientific Reports
11. Guidance for good practice in the application of machine learning in development of toxicological quantitative structure-activity relationships (QSARs)
    Samuel J. Belfield, M Cronin, Steven J. Enoch, James W. Firman · 2023 · PLoS ONE
12. Structural and Physico-Chemical Interpretation (SPCI) of QSAR Models and Its Comparison with Matched Molecular Pair Analysis
    Павел Геннадьевич Полищук, O.V. Tinkov, Tatiana Khristova, Liudmyla N. Ognichenko, Anna P. Kosinskaya, Alexandre A. Varnek, V. Е. Kuz’min · 2016 · Journal of Chemical Information and Modeling
13. A Perspective and a New Integrated Computational Strategy for Skin Sensitization Assessment
    Vinícius M. Alves, Stephen J. Capuzzi, Rodolpho C. Braga, Joyce Villa Verde Bastos Borba, Arthur C. Silva, Thomas Luechtefeld, Thomas Härtung, Carolina Horta Andrade, Eugene N. Muratov, Alexander Tropsha · 2018 · ACS Sustainable Chemistry & Engineering
14. Implementation of a dynamic intestinal gut-on-a-chip barrier model for transport studies of lipophilic dioxin congeners
    Kornphimol Kulthong, Loes P. M. Duivenvoorde, Barbara Z. Mizera, Deborah Rijkers, Guillaume ten Dam, Gerlof Oegema, Tomasz Puzyn, Hans Bouwmeester, Meike van der Zande · 2018 · RSC Advances
15. Chapter 5. Concepts and Applications of Conformal Prediction in Computational Drug Discovery
    Isidro Cortés‐Ciriano, Andreas Bender · 2020 · RSC drug discovery/RSC drug discovery series
16. Linear Regression Model Development for Analysis of Asymmetric Copper-Bisoxazoline Catalysis
    Jacob Werth, Matthew S. Sigman · 2021 · ACS Catalysis
17. Using molecular embeddings in QSAR modeling: does it make a difference?
    María Virginia Sabando, Ignacio Ponzoni, Evangelos Milios, Axel J. Soto · 2021 · Briefings in Bioinformatics
18. QSAR, Molecular Docking, MD Simulation and MMGBSA Calculations Approaches to Recognize Concealed Pharmacophoric Features Requisite for the Optimization of ALK Tyrosine Kinase Inhibitors as Anticancer Leads
    Rahul D. Jawarkar, Praveen Sharma, Neetesh Kumar Jain, Ajaykumar Gandhi, Nobendu Mukerjee, Aamal A. Al‐Mutairi, Magdi E. A. Zaki, Sami A. Al‐Hussain, Abdul Samad, Vijay Hariram Masand et al. · 2022 · Molecules
19. AI-enhanced chemical paradigm: From molecular graphs to accurate prediction and mechanism
    Zhi Zhen Huang, Yu Peng Jiang, Wei He, Jie Yu, Siwei Deng, Chun Yang, Weiwei Zhu, Xiao Shao · 2023 · Journal of Hazardous Materials
20. Advancing organic photovoltaic cells for a sustainable future: The role of artificial intelligence (AI) and deep learning (DL) in enhancing performance and innovation
    Hussein Togun, Ali Basem, Muhsin J. Jweeg, Nirmalendu Biswas, Azher M. Abed, Dipankar Paul, Hayder Ibrahim Mohammed, A.K. Chattopadhyay, Bhupendra Kumar Sharma, Tuqa Abdulrazzaq · 2025 · Solar Energy
21. Origin of the OECD Principles for QSAR Validation and Their Role in Changing the QSAR Paradigm Worldwide: An Historical Overview
    Paola Gramatica · 2025 · Journal of Chemometrics
22. Chemprop v2: An Efficient, Modular Machine Learning Package for Chemical Property Prediction
    David Graff, Nathan K. Morgan, Jackson W. Burns, Anna C. Doner, Brian Li, Shih‐Cheng Li, Joel Manu, Angiras Menon, Hao‐Wei Pang, Haoyang Wu et al. · 2025 · Journal of Chemical Information and Modeling
23. Theoretical Prediction of the Complex P-Glycoprotein Substrate Efflux Based on the Novel Hierarchical Support Vector Regression Scheme
    Chun Chen, Ming-Han Lee, Ching‐Feng Weng, Max K. Leong · 2018 · Molecules
24. Application of chemometric methods and QSAR models to support pesticide risk assessment starting from ecotoxicological datasets
    Francesco Galimberti, Angelo Moretto, Ester Papa · 2020 · Water Research
25. In silico assessment of adverse drug reactions and associated mechanisms
    Ivanov, Sergey M., Lagunin, Alexey A., Poroikov, Vladimir V. · 2016 · Drug Discovery Today
26. COVID‐19: A systematic review and update on prevention, diagnosis, and treatment
    Hooman Aghamirza Moghim Aliabadi, Reza Eivazzadeh‐Keihan, Arezoo Beig Parikhani, Sara Fattahi Mehraban, Ali Reza Maleki, Sepideh Fereshteh, Masoume Bazaz, Ashkan Zolriasatein, Bahareh Bozorgnia, Saman Rahmati et al. · 2022 · MedComm
27. The use of a quantitative structure-activity relationship (QSAR) model to predict GABA-A receptor binding of newly emerging benzodiazepines
    Laura J. Waters, Kieran R. Manchester, Peter Maskell, Caroline Haegeman, Shozeb M Haider · 2017 · Science & Justice
28. In Silico Prediction of PAMPA Effective Permeability Using a Two-QSAR Approach
    Cheng-Ting Chi, Ming-Han Lee, Ching‐Feng Weng, Max K. Leong · 2019 · International Journal of Molecular Sciences
29. Entering the era of computationally driven drug development
    Neha V. Maharao, Victor G. Antontsev, Matthew R. Wright, Jyotika Varshney · 2020 · Drug Metabolism Reviews
30. An Integrated High Throughput Experimentation/Predictive QSAR Modeling Approach to ansa-Zirconocene Catalysts for Isotactic Polypropylene
    Christian Ehm, Antonio Vittoria, Georgy P. Goryunov, Vyatcheslav V. Izmer, Dmitry S. Kononovich, Oleg V. Samsonov, Rocco Di Girolamo, Peter H. M. Budzelaar, Alexander Z. Voskoboynikov, Vincenzo Busico et al. · 2020 · Polymers
31. Cytotoxicity of nitrogenous disinfection byproducts: A combined experimental and computational study
    Huachang Hong, Yuchen Lu, Xiaoyan Zhu, Qiang Wu, Lingmin Jin, Zhigang Jin, Xiaoxuan Wei, Guangcai Ma, Haiying Yu · 2022 · The Science of The Total Environment
32. Predicting chemical hazard across taxa through machine learning
    Jimeng Wu, Simone D’Ambrosi, Lorenz Ammann, Julita Stadnicka-Michalak, Kristin Schirmer, Marco Baity‐Jesi · 2022 · Environment International
33. Exploiting Endocytosis for Non-Spherical Nanoparticle Cellular Uptake
    Saad Niaz, Ben Forbes, Bahijja Tolulope Raimi‐Abraham · 2022 · Nanomanufacturing
34. Machine Learning in Chemoinformatics and Medicinal Chemistry
    Raquel Rodríguez-Pérez, Filip N. Miljković, Jürgen Bajorath · 2022 · Annual Review of Biomedical Data Science
35. Comparison of various methods for validity evaluation of QSAR models
    Shadi Shayanfar, Ali Shayanfar · 2022 · BMC Chemistry
36. Leveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance predictive toxicology
    Adrian J. Green, Martin J. Mohlenkamp, Jhuma Das, Meenal Chaudhari, Lisa Truong, Robert L. Tanguay, David M. Reif · 2021 · PLoS Computational Biology
37. Medical Imaging with Deep Learning for COVID- 19 Diagnosis: A Comprehensive Review
    Subrato Bharati, Prajoy Podder, Md. Rubaiyat Hossain Mondal, V. B. Surya Prasath · 2021 · arXiv (Cornell University)
38. Performance of machine learning algorithms for qualitative and quantitative prediction drug blockade of hERG1 channel
    Sören Wacker, Sergei Yu. Noskov · 2017 · Computational Toxicology
39. Can we accelerate medicinal chemistry by augmenting the chemist with Big Data and artificial intelligence?
    Ed Griffen, Alexander G. Dossetter, Andrew G. Leach, Shane Montague · 2018 · Drug Discovery Today
40. Insights into the Pharmacological Effects of Flavonoids: The Systematic Review of Computer Modeling
    Amir Taldaev, Roman Petrovich Terekhov, Ilya D. Nikitin, Анастасия Константиновна Жевлакова, I. A. Selivanova · 2022 · International Journal of Molecular Sciences
41. Knowledge-based approaches to drug discovery for rare diseases
    Vinícius M. Alves, Daniel Korn, Vera Pervitsky, Andrew Thieme, Stephen J. Capuzzi, Nancy Baker, Rada Y. Chirkova, Sean Ekins, Eugene N. Muratov, Anthony James Hickey et al. · 2021 · Drug Discovery Today
42. AI-based toxicity prediction models using ToxCast data: Current status and future directions for explainable models
    Donghyeon Kim, Jinhee Choi · 2025 · Toxicology
43. Parametrization of nanoparticles: development of full-particle nanodescriptors
    Kaido Tämm, Lauri Sikk, Jaanus Burk, Robert Ralló, Suman Pokhrel, Lutz Mädler, Janeck James Scott-Fordsmand, Peeter Burk, Tarmo Tamm · 2016 · Nanoscale
44. Deep Learning Techniques and COVID-19 Drug Discovery: Fundamentals, State-of-the-Art and Future Directions
    Mohammad Behdad Jamshidi, Ali Lalbakhsh, Jakub Talla, Zdeněk Peroutka, Sobhan Roshani, Vaclav Matousek, Saeed Roshani, Mirhamed Mirmozafari, Zahra Malek, Luigi La Spada et al. · 2021 · Studies in systems, decision and control
45. Simplex representation of molecular structure as universal QSAR/QSPR tool
    V. Е. Kuz’min, Anatoly G. Artemenko, Liudmyla N. Ognichenko, Alexander I. Hromov, Anna P. Kosinskaya, Sergii I. Stelmakh, Zoe L. Sessions, Eugene N. Muratov · 2021 · Structural Chemistry
46. Computational Methods for the Interaction between Cyclodextrins and Natural Compounds: Technology, Benefits, Limitations, and Trends
    Botian Ding, Yuandong Yu, Sheng Geng, Benguo Liu, Youjin Hao, Guizhao Liang · 2022 · Journal of Agricultural and Food Chemistry
47. Application of Deep Neural Network Models in Drug Discovery Programs
    Christoph Grebner, Hans Matter, Daniel Kofink, Jan Wenzel, Friedemann Schmidt, Gerhard Heßler · 2021 · ChemMedChem
48. Rational design of stapled antimicrobial peptides
    Yuhao You, Hongyu Liu, Youzhuo Zhu, Heng Zheng · 2023 · Amino Acids
49. The Bigger Fish: A Comparison of Meta-Learning QSAR Models on Low-Resourced Aquatic Toxicity Regression Tasks
    Thalea Schlender, Markus Viljanen, Jan N. van Rijn, Felix Mohr, Willie J.G.M. Peijnenburg, Holger H. Hoos, Emiel Rorije, Albert Wong · 2023 · Environmental Science & Technology
50. A review of quantitative structure–property relationships for the fate of ionizable organic chemicals in water matrices and identification of knowledge gaps
    Tom M. Nolte, Ad M.J. Ragas · 2017 · Environmental Science Processes & Impacts
51. Evaluation of the applicability of existing (Q)SAR models for predicting the genotoxicity of pesticides and similarity analysis related with genotoxicity of pesticides for facilitating of grouping and read across
    Romualdo Benigni, Chiara Laura Battistelli, Cecilia Bossa, Alessandro Giuliani, Elena Fioravanzo, Arianna Bassan, Mojca Fuart Gatnik, James F. Rathman, Chihae Yang, Olga Tcheremenskaia · 2019 · EFSA Supporting Publications
52. Evaluation of the applicability of existing (Q)SAR models for predicting the genotoxicity of pesticides and similarity analysis related with genotoxicity of pesticides for facilitating of grouping and read across: An EFSA funded project
    Romualdo Benigni, Rositsa Serafimova, Juan Manuel Parra Morte, Chiara Laura Battistelli, Cecilia Bossa, Alessandro Giuliani, Elena Fioravanzo, Arianna Bassan, Mojca Fuart Gatnik, James F. Rathman et al. · 2020 · Regulatory Toxicology and Pharmacology
53. High-Throughput Experimentation in Olefin Polymerization Catalysis: Facing the Challenges of Miniaturization
    Christian Ehm, Alessio Mingione, Antonio Vittoria, Francesco Zaccaria, Roberta Cipullo, Vincenzo Busico · 2020 · Industrial & Engineering Chemistry Research
54. Current Status and Future Challenges in Molecular Design for Reduced Hazard
    Philip Coish, Bryan W. Brooks, Evan P. Gallagher, Terrance J. Kavanagh, Adelina M. Voutchkova‐Kostal, Julie Beth Zimmerman, Paul T. Anastas · 2016 · ACS Sustainable Chemistry & Engineering
55. Novel Directions in Free Energy Methods and Applications
    Kira A. Armacost, Sereina Riniker, Zoe Cournia · 2020 · Journal of Chemical Information and Modeling
56. Machine Learning Applications for Chemical Reactions
    Sanggil Park, Herim Han, Hyungjun Kim, Sunghwan Choi · 2022 · Chemistry - An Asian Journal
57. Molecular Dynamic Simulations for Biopolymers with Biomedical Applications
    Ramón Garduño‐Juárez, David Octavio Tovar-Anaya, Jose Manuel Perez‐Aguilar, Luis Fernando Lozano-Aguirre, Rafael A. Zubillaga, Marco Antonio Álvarez-Pérez, Eduardo Villarreal-Ramírez · 2024 · Polymers
58. Vina-GPU 2.1: Towards Further Optimizing Docking Speed and Precision of AutoDock Vina and Its Derivatives
    Shidi Tang, Ji Ding, Xiangyu Zhu, Zheng Wang, Haitao Zhao, Jiansheng Wu · 2024 · IEEE Transactions on Computational Biology and Bioinformatics
59. Improved Chemical Structure–Activity Modeling Through Data Augmentation
    Isidro Cortés‐Ciriano, Andreas Bender · 2015 · Journal of Chemical Information and Modeling
60. Public (Q)SAR Services, Integrated Modeling Environments, and Model Repositories on the Web: State of the Art and Perspectives for Future Development
    Igor V. Tetko, Uko Maran, Alexander Tropsha · 2016 · Molecular Informatics
61. QSAR-derived affinity fingerprints (part 1): fingerprint construction and modeling performance for similarity searching, bioactivity classification and scaffold hopping
    Ctibor Škuta, Isidro Cortés‐Ciriano, Wim Dehaen, P. Kříž, Gerard J. P. van Westen, Igor V. Tetko, Andreas Bender, Daniel Svozil · 2020 · Journal of Cheminformatics
62. Chemists: AI Is Here; Unite To Get the Benefits
    Ed Griffen, Alexander G. Dossetter, Andrew G. Leach · 2020 · Journal of Medicinal Chemistry
63. ADMET Predictability at Boehringer Ingelheim: State‐of‐the‐Art, and Do Bigger Datasets or Algorithms Make a Difference?
    Stevan Aleksić, Daniel Seeliger, J. B. Brown · 2021 · Molecular Informatics
64. Predicting Prenatal Developmental Toxicity Based On the Combination of Chemical Structures and Biological Data
    Heather L. Ciallella, Daniel P. Russo, Swati Sharma, Yafan Li, Eddie D. Sloter, Len Sweet, Heng Huang, Hao Zhu · 2022 · Environmental Science & Technology
65. Regulatory acceptance of in silico approaches for the safety assessment of cosmetic-related substances
    Gellatly, Nichola, Sewell, Fiona · 2019 · Computational Toxicology
66. Toward Interpretable Machine Learning Models for Materials Discovery
    Mikulskis, Paulius, Alexander, Morgan R., Winkler, David Alan · 2019 · Advanced Intelligent Systems
67. Updates on Drug Designing Approach Through Computational Strategies: a Review
    Iqbal Azad, Tahmeena Khan, Naseem Saud Ahmad, Abdul Rahman Khan, Yusuf Akhter · 2023 · Future Science OA
68. A Survey on Computational Methods in Drug Discovery for Neurodegenerative Diseases
    Caterina Vicidomini, Francesco Fontanella, Tiziana D’Alessandro, Giovanni Nicola Roviello · 2024 · Biomolecules
69. Overview of Computational Toxicology Methods Applied in Drug and Green Chemical Discovery
    José Ignacio Bueso-Bordils, Gerardo M. Antón-Fos, Rafael V. Martín-Algarra, P. Aleman · 2024 · Journal of Xenobiotics
70. 2D-QSAR and 3D-QSAR Analyses for EGFR Inhibitors
    Manman Zhao, Lin Wang, Linfeng Zheng, Mengying Zhang, Chun Qiu, Yuhui Zhang, Dongshu Du, Bing Niu · 2017 · BioMed Research International
71. QSAR study of 2,4-dihydro-3H-1,2,4-triazol-3-ones derivatives as angiotensin II AT1 receptor antagonists based on the Monte Carlo method
    V. Stoičkov, Dijana R. Stojanović, Ivan S. Tasic, Sandra Šarić, Dina Radenković, Petar Babović, Dusan T. Sokolovic, Aleksandar M. Veselinović · 2017 · Structural Chemistry
72. In Silico Studies Designed to Select Sesquiterpene Lactones with Potential Antichagasic Activity from an In‐House Asteraceae Database
    Chonny Herrera‐Acevedo, Luciana Scotti, Marcus Tullius Scotti · 2018 · ChemMedChem
73. Modeling pesticides toxicity to Sheepshead minnow using QSAR
    Yang Lü, Yinghuan Wang, Weiyu Hao, Jing Chang, Yifan Pan, Jianzhong Li, Huili Wang · 2020 · Ecotoxicology and Environmental Safety
74. In silico models for genotoxicity and drug regulation
    Romualdo Benigni, Arianna Bassan, Manuela Pavan · 2020 · Expert Opinion on Drug Metabolism & Toxicology
75. Accuracy or novelty: what can we gain from target-specific machine-learning-based scoring functions in virtual screening?
    Chao Shen, Gaoqi Weng, Xujun Zhang, Elaine Lai‐Han Leung, Xiaojun Yao, Jinping Pang, Xin Chai, Dan Li, Ercheng Wang, Dongsheng Cao et al. · 2021 · Briefings in Bioinformatics
76. The effect of noise on the predictive limit of QSAR models
    Scott Kolmar, Chris Grulke · 2021 · Journal of Cheminformatics
77. Leveraging nonstructural data to predict structures and affinities of protein–ligand complexes
    Joseph M. Paggi, Julia A. Belk, Scott A. Hollingsworth, Nicolas Villanueva, Alexander S. Powers, Mary Jo Clark, Augustine Chemparathy, Jonathan Tynan, Thomas K. Lau, Roger K. Sunahara et al. · 2021 · Proceedings of the National Academy of Sciences
78. In Silico Approaches for Predictive Toxicology
    Ramakrishnan Parthasarathi, Alok Dhawan · 2017 · Elsevier eBooks
79. MAIP: a web service for predicting blood‐stage malaria inhibitors
    Nicolas Bosc, Eloy Félix, Ricardo Arcila, David Méndez, Martin R. Saunders, Darren V. S. Green, Jason Ochoada, Anang A. Shelat, Éric Martin, Preeti Iyer et al. · 2021 · Journal of Cheminformatics
80. Improved Chemical Prediction from Scarce Data Sets via Latent Space Enrichment
    Iovanac, Nicolae C., Savoie, Brett M. · 2019 · The Journal of Physical Chemistry A
81. Precision and recall oncology: combining multiple gene mutations for improved identification of drug-sensitive tumours
    Naulaerts, Stefan, Dang, Cuong C., Ballester, Pedro J. · 2017 · Oncotarget
82. Molecular modelling of the thermophysical properties of fluids: expectations, limitations, gaps and opportunities
    Marcus J. Tillotson, Nikolaos I. Diamantonis, Corneliu Buda, Leslie W. Bolton, Erich A. Müller · 2023 · Physical Chemistry Chemical Physics
83. Evaluating uncertainty-based active learning for accelerating the generalization of molecular property prediction
    Tianzhixi Yin, Gihan Panapitiya, Elizabeth Coda, Emily Saldanha · 2023 · Journal of Cheminformatics
84. In silico off-target profiling for enhanced drug safety assessment
    Jin Liu, Yike Gui, Jingxin Rao, Jingjing Sun, Gang Wang, Qun Ren, Ning Qu, Buying Niu, Zhiyi Chen, Xia Sheng et al. · 2024 · Acta Pharmaceutica Sinica B
85. Machine learning-driven QSAR models for predicting the cytotoxicity of five common microplastics
    Chengze Liu, Cheng Zong, Shuang Chen, Jiangliang Chu, Yifan Yang, Yong Liang Pan, Beilei Yuan, Huazhong Zhang · 2024 · Toxicology
86. Assessment of river sediment toxicity: Combining empirical zebrafish embryotoxicity testing with in silico toxicity characterization
    Sanja Babić, Josip Barišić, Draženka Stipaničev, Siniša Repec, Mario Lovrić, Olga Malev, Dalma Martinović‐Weigelt, Rozelindra Čož‐Rakovac, Göran I.V. Klobučar · 2018 · The Science of The Total Environment
87. On the Misleading Use of for QSAR Model Comparison
    Viviana Consonni, Roberto Todeschini, Davide Ballabio, Francesca Grisoni · 2018 · Molecular Informatics
88. Predicting Binding from Screening Assays with Transformer Network Embeddings
    Paul Morris, Rachel St. Clair, William Edward Hahn, Elan Barenholtz · 2020 · Journal of Chemical Information and Modeling
89. Applications of Artificial Intelligence in Drug Design: Opportunities and Challenges
    Morgan C. Thomas, Andrew Boardman, Miguel García-Ortegón, Hongbin Yang, Chris de Graaf, Andreas Bender · 2021 · Methods in molecular biology
90. Structure-guided unravelling: Phenolic hydroxyls contribute to reduction of acrylamide using multiplex quantitative structure–activity relationship modelling
    Yu Zhang, Mengmeng Huang, Qiao Wang, Jun Cheng · 2015 · Food Chemistry
91. In silico Prediction of Skin Sensitization: Quo vadis?
    Giang Huong Ta, Ching‐Feng Weng, Max K. Leong · 2021 · Frontiers in Pharmacology
92. A brief guide to machine learning for antibiotic discovery
    Gary Liu, Jonathan M Stokes · 2022 · Current Opinion in Microbiology
93. Data-Driven Cheminformatics Models for Predicting Bioactivity of Natural Compounds in Oncology
    Salvation Ifechukwude Atalor · 2022 · International Journal of Scientific Research and Modern Technology.
94. The kernel-weighted local polynomial regression (KwLPR) approach: an efficient, novel tool for development of QSAR/QSAAR toxicity extrapolation models
    Agnieszka Gajewicz, Supratik Kar, Magdalena Piotrowska, Jerzy Leszczyński · 2021 · Journal of Cheminformatics
95. The challenges of generalizability in artificial intelligence for ADME/Tox endpoint and activity prediction
    David Z Huang, J. Christian Baber, Sogole Sami Bahmanyar · 2021 · Expert Opinion on Drug Discovery
96. Design and Development of Novel Nutraceuticals: Current Trends and Methodologies
    Thalia Tsiaka, Eftichia Kritsi, Konstantinos Tsiantas, Paris Christodoulou, Vassilia J. Sinanoglou, Panagiotis G. Zoumpoulakis · 2022 · Nutraceuticals
97. Monte Carlo method based QSAR modelling of natural lipase inhibitors using hybrid optimal descriptors
    Ashwani Kumar, Shilpi Chauhan · 2017 · SAR and QSAR in environmental research
98. Applications of Chemoinformatics in Predictive Toxicology for Regulatory Purposes, Especially in the Context of the EU REACH Legislation
    Rafael Gozalbes, Jesus Vicente de Julián‐Ortiz · 2017 · International Journal of Quantitative Structure-Property Relationships
99. Discovering Highly Potent Molecules from an Initial Set of Inactives Using Iterative Screening
    Isidro Cortés‐Ciriano, Nicholas C. Firth, Andreas Bender, Oliver P Watson · 2018 · Journal of Chemical Information and Modeling
100. Antimicrobial peptide similarity and classification through rough set theory using physicochemical boundaries
     Kyle Boone, Kyle Vincent Camarda, Paulette Spencer, Candan Tamerler · 2018 · BMC Bioinformatics