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

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Items 101–200 of 1,001

1. Predicting Synergism of Cancer Drug Combinations Using NCI-ALMANAC Data
   Pavel Sidorov, Stefan Naulaerts, Jérémy Ariey-Bonnet, Eddy Pasquier, Pedro J. Ballester · 2019 · Frontiers in Chemistry
2. How Precise Are Our Quantitative Structure–Activity Relationship Derived Predictions for New Query Chemicals?
   Kunal Roy, Pravin Sundarao Ambure, Supratik Kar · 2018 · ACS Omega
3. Ecotoxicological assessment of pharmaceuticals and personal care products using predictive toxicology approaches
   Supratik Kar, Hans Sanderson, Kunal Roy, Emilio Benfenati, Jerzy Leszczyński · 2020 · Green Chemistry
4. First report of q-RASAR modeling toward an approach of easy interpretability and efficient transferability
   Arkaprava Banerjee, Kunal Roy · 2022 · Molecular Diversity
5. Artificial Intelligence-Based Toxicity Prediction of Environmental Chemicals: Future Directions for Chemical Management Applications
   Jaeseong Jeong, Jinhee Choi · 2022 · Environmental Science & Technology
6. How important is to detect systematic error in predictions and understand statistical applicability domain of QSAR models?
   Kunal Roy, Pravin Sundarao Ambure, Rahul Balasaheb Aher · 2017 · Chemometrics and Intelligent Laboratory Systems
7. A review on machine learning methods for in silico toxicity prediction
   Gabriel Idakwo, Joseph Luttrell, Minjun Chen, Huixiao Hong, Zhaoxian Zhou, Ping Gong, Chaoyang Zhang · 2018 · Journal of Environmental Science and Health Part C
8. The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives
   Sarfaraz K. Niazi · 2023 · Drug Design Development and Therapy
9. Inductive transfer learning for molecular activity prediction: Next-Gen QSAR Models with MolPMoFiT
   Xinhao Li, Denis Fourches · 2020 · Journal of Cheminformatics
10. Computational platform Way2Drug: from the prediction of biological activity to drug repurposing
    Dmitry S. Druzhilovskiy, Anastasia V. Rudik, Dmitry A. Filimonov, Tatyana A. Gloriozova, Alexey A. Lagunin, Alexander V. Dmitriev, Pavel V. Pogodin, Varvara Dubovskaja, Sergey M. Ivanov, Olga A. Tarasova et al. · 2017 · Russian Chemical Bulletin
11. In silico Methods for Design of Kinase Inhibitors as Anticancer Drugs
    Žarko Gagić, Dušan Ružić, Nemanja Djoković, Teodora Djikić, Katarina M. Nikolic · 2020 · Frontiers in Chemistry
12. Bioinformatics Approaches for Anti-cancer Drug Discovery
    Kening Li, Yuxin Du, Lu Li, Dong‐Qing Wei · 2019 · Current Drug Targets
13. Concepts and applications of chemical fingerprint for hit and lead screening
    Jingbo Yang, Yiyang Cai, Kairui Zhao, Hongbo Xie, Xiujie Chen · 2022 · Drug Discovery Today
14. Construction of a web-based nanomaterial database by big data curation and modeling friendly nanostructure annotations
    Xiliang Yan, Alexander Sedykh, Wenyi Wang, Bing Yan, Hao Zhu · 2020 · Nature Communications
15. Simulating drug concentrations in PDMS microfluidic organ chips
    Grant, Jennifer, Özkan, Alican, Oh, Crystal, Mahajan, Gautam, Prantil-Baun, Rachelle, Ingber, Donald E. · 2021 · Lab on a Chip
16. Alarms about structural alerts
    Alves, Vinicius M., Muratov, Eugene N., Capuzzi, Stephen J., Politi, Regina, Low, Yen, Braga, Rodolpho C., Zakharov, Alexey V., Sedykh, Alexander, Mokshyna, Elena, Farag, Sherif et al. · 2016 · Green Chemistry
17. Is it possible to improve the quality of predictions from an “intelligent” use of multiple QSAR/QSPR/QSTR models?
    Kunal Roy, Pravin Sundarao Ambure, Supratik Kar, Probir Kumar Ojha · 2018 · Journal of Chemometrics
18. High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage
    Yunjia Lai, Jeremy P. Koelmel, Douglas I. Walker, Elliott James Price, Stefano Papazian, Katherine E. Manz, Delia Castilla-Fernández, John A. Bowden, Vladimir A. Nikiforov, Arthur David et al. · 2024 · Environmental Science & Technology
19. Interpretable Deep Learning in Drug Discovery
    Kristina Preuer, Günter Klambauer, Friedrich Rippmann, Sepp Hochreiter, Thomas Unterthiner · 2019 · Lecture notes in computer science
20. QSAR-driven design, synthesis and discovery of potent chalcone derivatives with antitubercular activity
    Marcelo do Nascimento Gomes, Rodolpho C. Braga, Edyta M. Grzelak, Bruno J. Neves, Eugene N. Muratov, Rui Ma, Larry L. Klein, Sang‐Hyun Cho, Guilherme Roberto de Oliveira, Scott Gary Franzblau et al. · 2017 · European Journal of Medicinal Chemistry
21. From Target to Drug: Generative Modeling for the Multimodal Structure-Based Ligand Design
    Miha Škalič, Davide Sabbadin, Boris Sattarov, Simone Sciabola, Gianni De Fabritiis · 2019 · Molecular Pharmaceutics
22. Computational Methods and Tools in Antimicrobial Peptide Research
    Pietro G. A. Aronica, Lauren Reid, Nirali Desai, Jianguo Li, Stephen J. Fox, Shilpa Yadahalli, Jonathan W. Essex, Chandra Shekhar Verma · 2021 · Journal of Chemical Information and Modeling
23. Identifying Structure–Property Relationships through SMILES Syntax Analysis with Self-Attention Mechanism
    Shuangjia Zheng, Xin Yan, Yuedong Yang, Jun Xu · 2019 · Journal of Chemical Information and Modeling
24. Accelerating AutoDock Vina with GPUs
    Shidi Tang, Ruiqi Chen, Lin Mengru, Lin Qingde, Yanxiang Zhu, Ji Ding, Jiansheng Wu, Hu Haifeng, Ling Ming · 2022 · Molecules
25. Development of quantitative structure activity relationship (QSAR) model for disinfection byproduct (DBP) research: A review of methods and resources
    Baiyang Chen, Tian Zhang, Tom Bond, Yiqun Gan · 2015 · Journal of Hazardous Materials
26. Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records
    Bean, Daniel M., Wu, Honghan, Iqbal, Ehtesham, Dzahini, Olubanke, Ibrahim, Zina M., Broadbent, Matthew, Stewart, Robert, Dobson, Richard J. B. · 2017 · Scientific Reports
27. Nanochemistry advancing photon conversion in rare-earth nanostructures for theranostics
    Ruichan Lv, Micah E. Raab, Yanxing Wang, Jie Tian, Jun Lin, Paras N. Prasad · 2022 · Coordination Chemistry Reviews
28. Linking synthesis and structure descriptors from a large collection of synthetic records of zeolite materials
    Koki Muraoka, Yuki Sada, Daiki Miyazaki, Watcharop Chaikittisilp, Tatsuya Okubo · 2019 · Nature Communications
29. Methods for generating and applying pharmacophore models as virtual screening filters and for bioactivity profiling
    Anna Vuorinen, Daniela Schuster · 2014 · Methods
30. Virtual Screening Techniques in Drug Discovery: Review and Recent Applications
    Sheisi Fonseca Leite da Silva Rocha, Carolina G. Olanda, Harold Hilarion Fokoue, Carlos Maurício R. Sant’Anna · 2019 · Current Topics in Medicinal Chemistry
31. PeptideBERT: A Language Model Based on Transformers for Peptide Property Prediction
    Chakradhar Guntuboina, Adrita Das, Parisa Mollaei, Seong Won Kim, Amir Barati Farimani · 2023 · The Journal of Physical Chemistry Letters
32. Rapid Life-Cycle Impact Screening Using Artificial Neural Networks
    Runsheng Song, Arturo A. Keller, Sangwon Suh · 2017 · Environmental Science & Technology
33. Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace
    Natesh Singh, Ludovic Chaput, Bruno O. Villoutreix · 2020 · Briefings in Bioinformatics
34. Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors
    Anna Cichońska, Balaguru Ravikumar, Elina Parri, Sanna Timonen, Tapio Pahikkala, Antti Airola, Krister Wennerberg, Juho Rousu, Tero A. Aittokallio · 2017 · PLoS Computational Biology
35. QSAR-Co: An Open Source Software for Developing Robust Multitasking or Multitarget Classification-Based QSAR Models
    Pravin Sundarao Ambure, Amit Kumar Halder, Humberto González‐Díaz, M. Natália D. S. Cordeiro · 2019 · Journal of Chemical Information and Modeling
36. Molecule Property Prediction Based on Spatial Graph Embedding
    Xiaofeng Wang, Zhen Li, Mingjian Jiang, Shuang Wang, Shugang Zhang, Zhiqiang Wei · 2019 · Journal of Chemical Information and Modeling
37. Transfer and Multi-task Learning in QSAR Modeling: Advances and Challenges
    Rodolfo da Silva Simões, Vinícius Gonçalves Maltarollo, Patrícia Rufino Oliveira, Káthia M. Honório · 2018 · Frontiers in Pharmacology
38. Drug‐Like ProteinProtein Interaction Modulators: Challenges and Opportunities for Drug Discovery and Chemical Biology
    Bruno O. Villoutreix, Mélaine A. Kuenemann, Jean‐Luc Poyet, Heriberto Bruzzoni‐Giovanelli, Céline M. Labbé, David Lagorce, Olivier Spérandio, Maria A. Miteva · 2014 · Molecular Informatics
39. Combining machine learning and high-throughput experimentation to discover photocatalytically active organic molecules
    Xiaobo Li, Phillip M. Maffettone, Yu Che, Tao Liu, Linjiang Chen, Andrew Ian Cooper · 2021 · Chemical Science
40. Support Vector Machine Classification and Regression Prioritize Different Structural Features for Binary Compound Activity and Potency Value Prediction
    Raquel Rodríguez-Pérez, Martin Vogt, Jürgen Bajorath · 2017 · ACS Omega
41. Advancing pharmaceutical research: A comprehensive review of cutting-edge tools and technologies
    Chen, Jinghong, Lin, Anqi, Luo, Peng · 2024 · Current Pharmaceutical Analysis
42. The importance of the glycosylation of antimicrobial peptides: natural and synthetic approaches
    Natalia G. Bednarska, Brendan William Wren, Sam Willcocks · 2017 · Drug Discovery Today
43. Synergistic and Antagonistic Drug Combinations against SARS-CoV-2
    Tesia Bobrowski, Lu Chen, Richard T. Eastman, Zina Itkin, Paul Shinn, Catherine Z. Chen, Hui Guo, Wei Zheng, Sam Michael, Anton M. Simeonov et al. · 2020 · Molecular Therapy
44. Computer-aided Discovery of Peptides that Specifically Attack Bacterial Biofilms
    Evan F. Haney, Yoan Brito‐Sánchez, Michael J. Trimble, Sarah Mansour, Artem R Cherkasov, Robert E. W. Hancock · 2018 · Scientific Reports
45. Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies
    Davide Bassani, Stefano Moro · 2023 · Molecules
46. Generative and reinforcement learning approaches for the automated de novo design of bioactive compounds
    Maria V. Korshunova, Niles Huang, Stephen J. Capuzzi, Dmytro S. Radchenko, Olena Savych, Yurii S. Moroz, Carrow I. Wells, Timothy Mark Willson, Alexander Tropsha, Olexandr Isayev · 2022 · Communications Chemistry
47. History of EPI Suite™ and future perspectives on chemical property estimation in US Toxic Substances Control Act new chemical risk assessments
    Marcella L. Card, Vicente Gomez‐Alvarez, Wen-Hsiung Lee, David G. Lynch, Nerija Orentas, Mari Titcombe, Edmund M. Wong, Robert S. Boethling · 2017 · Environmental Science Processes & Impacts
48. Best Practices of Computer-Aided Drug Discovery: Lessons Learned from the Development of a Preclinical Candidate for Prostate Cancer with a New Mechanism of Action
    Ban, Fuqiang, Dalal, Kush, Li, Huifang, LeBlanc, Eric, Rennie, Paul S., Cherkasov, Artem · 2017 · Journal of Chemical Information and Modeling
49. Prediction of cytochrome P450 mediated metabolism
    Lars Olsen, Chris Oostenbrink, Flemming Steen Jørgensen · 2015 · Advanced Drug Delivery Reviews
50. Critical assessment of AI in drug discovery
    W. Patrick Walters, Regina Barzilay · 2021 · Expert Opinion on Drug Discovery
51. Molecular simulations in drug delivery: Opportunities and challenges
    Ratna S. Katiyar, Prateek K. Jha · 2018 · Wiley Interdisciplinary Reviews Computational Molecular Science
52. Quantum chemical descriptors in quantitative structure–activity relationship models and their applications
    Liangliang Wang, Junjie Ding, Li Pan, Dongsheng Cao, Hui Jiang, Xiaoqin Ding · 2021 · Chemometrics and Intelligent Laboratory Systems
53. Zeta potentials (ζ) of metal oxide nanoparticles: A meta-analysis of experimental data and a predictive neural networks modeling
    Natalia Sizochenko, Alicja Mikołajczyk, Michael Syzochenko, Tomasz Puzyn, Jerzy Leszczyński · 2021 · NanoImpact
54. Machine Learning and Big Data Provide Crucial Insight for Future Biomaterials Discovery and Research
    Jacob Kerner, Alan B. Dogan, Horst A. von Recum · 2020 · Acta Biomaterialia
55. Predicting Nano–Bio Interactions by Integrating Nanoparticle Libraries and Quantitative Nanostructure Activity Relationship Modeling
    Wenyi Wang, Alexander Sedykh, Hainan Sun, Linlin Zhao, Daniel P. Russo, Hongyu Zhou, Bing Yan, Hao Zhu · 2017 · ACS Nano
56. Comparative study between deep learning and QSAR classifications for TNBC inhibitors and novel GPCR agonist discovery
    Lun K. Tsou, Shiu-Hwa Yeh, Shau‐Hua Ueng, Chun‐Ping Chang, Jen‐Shin Song, Mine-Hsine Wu, Hsiao-Fu Chang, Sheng-Ren Chen, Chuan Shih, Chiung‐Tong Chen et al. · 2020 · Scientific Reports
57. Comparative analysis of molecular fingerprints in prediction of drug combination effects
    Bulat Zagidullin, Ziyan Wang, Yuanfang Guan, Esa Pitkänen, Jing Ping Tang · 2021 · Briefings in Bioinformatics
58. Learnings from quantitative structure–activity relationship (QSAR) studies with respect to food protein-derived bioactive peptides: a review
    Alice B. Nongonierma, Richard J. Fitzgerald · 2016 · RSC Advances
59. Interaction prediction in structure-based virtual screening using deep learning
    Adam Gonczarek, Jakub M. Tomczak, Szymon Zaręba, Joanna Kaczmar-Michalska, Piotr Dąbrowski, Michał Walczak · 2017 · Computers in Biology and Medicine
60. An exploration strategy improves the diversity of de novo ligands using deep reinforcement learning: a case for the adenosine A2A receptor
    Xuhan Liu, Kai Ye, Herman van Vlijmen, Adriaan P. IJzerman, Gerard J. P. van Westen · 2019 · Journal of Cheminformatics
61. Deep Confidence: A Computationally Efficient Framework for Calculating Reliable Prediction Errors for Deep Neural Networks
    Isidro Cortés‐Ciriano, Andreas Bender · 2018 · Journal of Chemical Information and Modeling
62. MvMRL: a multi-view molecular representation learning method for molecular property prediction
    Ru Zhang, Yanmei Lin, Yijia Wu, Lei Deng, Hao Zhang, Mingzhi Liao, Yuzhong Peng · 2024 · Briefings in Bioinformatics
63. Tuning hERG Out: Antitarget QSAR Models for Drug Development
    Rodolpho C. Braga, Vinícius M. Alves, Meryck Felipe Brito da Silva, Eugene N. Muratov, Denis Fourches, Alexander Tropsha, Carolina Horta Andrade · 2014 · Current Topics in Medicinal Chemistry
64. Molecular Descriptors
    Andrea Mauri, Viviana Consonni, Roberto Todeschini · 2017 · Springer eBooks
65. Deep learning meets quantitative structure–activity relationship (QSAR) for leveraging structure-based prediction of solute rejection in organic solvent nanofiltration
    Gergő Ignácz, György Székely · 2022 · Journal of Membrane Science
66. Encodings and models for antimicrobial peptide classification for multi-resistant pathogens
    Sebastian Spänig, Dominik Heider · 2019 · BioData Mining
67. The Application of Modeling and Prediction to the Formation and Stability of Amorphous Solid Dispersions
    Kevin DeBoyace, Peter L. D. Wildfong · 2017 · Journal of Pharmaceutical Sciences
68. Towards a Digital Bioprocess Replica: Computational Approaches in Biopharmaceutical Development and Manufacturing
    Jens Smiatek, Alexander Jung, Erich Bluhmki · 2020 · Trends in biotechnology
69. The role and potential of computer-aided drug discovery strategies in the discovery of novel antimicrobials
    Samson O. Oselusi, Phumuzile Dube, Adeshina I. Odugbemi, Kolajo Adedamola Akinyede, Tosin L. Ilori, Elizabeth Oyebola Egieyeh, Nicole Remaliah Samantha Sibuyi, Mervin Meyer, Abram Madimabe Madiehe, Gerald J. Wyckoff et al. · 2024 · Computers in Biology and Medicine
70. Using Rule-Based Labels for Weak Supervised Learning
    Garrett B. Goh, Charles Siegel, Abhinav Vishnu, Nathan O. Hodas · 2018 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
71. Computational Approaches to Identify Structural Alerts and Their Applications in Environmental Toxicology and Drug Discovery
    Hongbin Yang, Chaofeng Lou, Weihua Li, Guixia Liu, Yun Tang · 2020 · Chemical Research in Toxicology
72. Machine Learning in Quantitative Protein–peptide Affinity Prediction: Implications for Therapeutic Peptide Design
    Zhongyan Li, Qingqing Miao, Fugang Yan, Yang Meng, Peng Zhou · 2018 · Current Drug Metabolism
73. Natural Products as Leads in Schistosome Drug Discovery
    Bruno J. Neves, Carolina Horta Andrade, Pedro Cravo · 2015 · Molecules
74. Machine learning approaches for elucidating the biological effects of natural products
    Ruihan Zhang, Xiao‐Li Li, Xing‐Jie Zhang, Huayan Qin, Wei‐Lie Xiao · 2020 · Natural Product Reports
75. Structure and dynamics in drug discovery
    Haixin Wei, James Andrew McCammon · 2024 · npj Drug Discovery.
76. XGraphBoost: Extracting Graph Neural Network-Based Features for a Better Prediction of Molecular Properties
    Daiguo Deng, Xiaowei Chen, Ruochi Zhang, Zengrong Lei, Xiaojian Wang, Fengfeng Zhou · 2021 · Journal of Chemical Information and Modeling
77. Nonantimicrobial Actions of Macrolides: Overview and Perspectives for Future Development
    Jennifer Ann Kricker, Clive Peter Page, Fridrik Runar Gardarsson, Ólafur Baldursson, Þórarinn Guðjónsson, Michael John Parnham · 2021 · Pharmacological Reviews
78. Meta-QSAR: a large-scale application of meta-learning to drug design and discovery
    Iván Olier, Noureddin Sadawi, G. Richard Bickerton, Joaquin Vanschoren, Crina Groşan, Larisa Soldatova, Ross D. King · 2017 · Machine Learning
79. 3D-QSAR studies on Maslinic acid analogs for Anticancer activity against Breast Cancer cell line MCF-7
    Sarfaraz Alam, Feroz Khan · 2017 · Scientific Reports
80. Descriptor Free QSAR Modeling Using Deep Learning With Long Short-Term Memory Neural Networks
    Suman Chakravarti, Sai Radha Mani Alla · 2019 · Frontiers in Artificial Intelligence
81. Harnessing Semi-Supervised Machine Learning to Automatically Predict Bioactivities of Per- and Polyfluoroalkyl Substances (PFASs)
    Hyuna Kwon, Zulfikhar A. Ali, Bryan M. Wong · 2022 · Environmental Science & Technology Letters
82. Chemoinformatics: Achievements and Challenges, a Personal View
    Johann Gasteiger · 2016 · Molecules
83. Pred-Skin: A Fast and Reliable Web Application to Assess Skin Sensitization Effect of Chemicals
    Rodolpho C. Braga, Vinícius M. Alves, Eugene N. Muratov, Judy A. Strickland, Nicole Kleinstreuer, Alexander Tropsha, Carolina Horta Andrade · 2017 · Journal of Chemical Information and Modeling
84. Discovery of New Anti-Schistosomal Hits by Integration of QSAR-Based Virtual Screening and High Content Screening
    Bruno J. Neves, Rafael Ferreira Dantas, Mário Roberto Senger, Cleber Camilo Melo-Filho, Walter César Góes Valente, Ana C. M. de Almeida, João M. Rezende-Neto, Elid F. C. Lima, Ross A. Paveley, Nicholas Furnham et al. · 2016 · Journal of Medicinal Chemistry
85. Sextans: A Streaming Accelerator for General-Purpose Sparse-Matrix Dense-Matrix Multiplication
    Linghao Song, Yuze Chi, Atefeh Sohrabizadeh, Young Choi, Jason Lau, Jason Cong · 2022 · ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA)
86. A new semi-automated workflow for chemical data retrieval and quality checking for modeling applications
    Domenico Gadaleta, Anna Lombardo, Cosimo Toma, Emilio Benfenati · 2018 · Journal of Cheminformatics
87. HiGNN: A Hierarchical Informative Graph Neural Network for Molecular Property Prediction Equipped with Feature-Wise Attention
    Weimin Zhu, Yi Zhang, Duancheng Zhao, Jianrong Xu, Ling Wang · 2022 · Journal of Chemical Information and Modeling
88. ToxAIcology - The evolving role of artificial intelligence in advancing toxicology and modernizing regulatory science
    Thomas Härtung · 2023 · ALTEX
89. Prediction of acute toxicity of emerging contaminants on the water flea Daphnia magna by Ant Colony Optimization–Support Vector Machine QSTR models
    Reza Aalizadeh, Peter C. von der Ohe, Νikolaos S. Τhomaidis · 2017 · Environmental Science Processes & Impacts
90. Best Practices for QSAR Model Reporting: Physical and Chemical Properties, Ecotoxicity, Environmental Fate, Human Health, and Toxicokinetics Endpoints
    Geven Piir, Iiris Kahn, Alfonso T. García‐Sosa, Sulev Sild, Priit Ahte, Uko Maran · 2018 · Environmental Health Perspectives
91. Deep learning for predicting toxicity of chemicals: a mini review
    Weihao Tang, Jingwen Chen, Zhongyu Wang, Hong‐Bin Xie, Huixiao Hong · 2018 · Journal of Environmental Science and Health Part C
92. Representing and describing nanomaterials in predictive nanoinformatics
    Ewelina Wyrzykowska, Alicja Mikołajczyk, Iseult Lynch, Nina Jeliazkova, Nikolay T. Kochev, Haralambos Sarimveis, Philip Doganis, Pantelis Karatzas, Antreas Afantitis, Georgia Melagraki et al. · 2022 · Nature Nanotechnology
93. Viscosity of Ionic Liquids: Theories and Models
    Na Gao, Ye Yang, Zhiyuan Wang, Xin Guo, Siqi Jiang, Jisheng Li, Yufeng Hu, Zhichang Liu, Chunming Xu · 2023 · Chemical Reviews
94. Applications of Quantitative Structure-Activity Relationships (QSAR) based Virtual Screening in Drug Design: A Review
    Patnala Ganga Raju Achary · 2020 · Mini-Reviews in Medicinal Chemistry
95. The “double cross-validation” software tool for MLR QSAR model development
    Kunal Roy, Pravin Sundarao Ambure · 2016 · Chemometrics and Intelligent Laboratory Systems
96. Molecular-Based Guide to Predict the pH of Eutectic Solvents: Promoting an Efficient Design Approach for New Green Solvents
    Tarek Lemaoui, Farah Abu Hatab, Ahmad S. Darwish, Ayoub Attoui, Nour El Houda Hammoudi, Ghaiath Almustafa, Mohamed Benaicha, Yacine Benguerba, Inas Muen AlNashef · 2021 · ACS Sustainable Chemistry & Engineering
97. Transformers enable accurate prediction of acute and chronic chemical toxicity in aquatic organisms
    Gustavsson, Mikael, Käll, Styrbjörn, Svedberg, Patrik, Inda-Diaz, Juan S., Molander, Sverker, Coria, Jessica, Backhaus, Thomas, Kristiansson, Erik · 2024 · Science Advances
98. Quantitative Structure–Activity Relationship (QSAR) Study Predicts Small-Molecule Binding to RNA Structure
    Zhengguo Cai, Martina Zafferani, Olanrewaju Akande, Amanda E. Hargrove · 2022 · Journal of Medicinal Chemistry
99. Computer-aided drug discovery
    Jürgen Bajorath · 2015 · F1000Research
100. Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning
     Sai Krishna Gottipati, Boris Sattarov, Sufeng Niu, Yashaswi Pathak, Haoran Wei, Shengchao Liu, Karam M. J. Thomas, Simon Blackburn, Connor W. Coley, Jian Tang et al. · 2020 · arXiv (Cornell University)