Computer science and chemistry overlap extensively in cheminformatics and molecular modeling
Extensive literature confirms that computer science and chemistry deeply overlap in the fields of cheminformatics and molecular modeling, particularly for drug discovery and predictive property modeling.
The retrieved papers consistently demonstrate that computer science, artificial intelligence, and machine learning are heavily integrated with chemistry, specifically through cheminformatics, molecular modeling, and computer-aided drug design. All papers support the claim, with zero papers providing refuting evidence.
J. Panteleev, Hua Gao, Lei Jia. Recent applications of machine learning in medicinal chemistry.. 2018. https://doi.org/10.1016/j.bmcl.2018.06.046
Applies machine learning and computer science tools to medicinal chemistry and drug discovery.
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Priyanka Banerjee, Vishal B. Siramshetty, Malgorzata N. Drwal, R. Preissner. Computational methods for prediction of in vitro effects of new chemical structures. 2016. https://doi.org/10.1186/s13321-016-0162-2
Utilizes computational approaches like machine learning and quantitative structure-activity relationships to predict chemical safety and activity.
Jinsong Shao, Qifeng Jia, Chen Xing, Yajie Hao, Li Wang. Molecular fragmentation as a crucial step in the AI-based drug development pathway. 2024. https://doi.org/10.1038/s42004-024-01109-2
Examines molecular fragmentation as a key intersection of computer science and life sciences in drug development.
Emily Xi Tan, Lam Bang Thanh Nguyen, Yubin Jin, Yan Lv, I. Phang, X. Ling. SERS Cheminformatics: Opportunities for Data-Driven Discovery and Applications. 2025. https://doi.org/10.1021/acscentsci.5c00785
Explores how cheminformatics combines chemical knowledge and computational methods for molecular modeling and analysis.
Ratul Bhowmik, Ravi Kant, A. Manaithiya, D. Saluja, Bharti Vyas, Ranajit Nath, K. Qureshi, S. Parkkila, A. Aspatwar. Navigating bioactivity space in anti-tubercular drug discovery through the deployment of advanced machine learning models and cheminformatics tools: a molecular modeling based retrospective study. 2023. https://doi.org/10.3389/fphar.2023.1265573
Discusses computational techniques like QSAR and molecular docking in drug discovery.
Sakander Hayat, S. Wazzan. A Computational Approach to Predictive Modeling Using Connection-Based Topological Descriptors: Applications in Coumarin Anti-Cancer Drug Properties. 2025. https://doi.org/10.3390/ijms26051827
Defines cheminformatics as a bridge between chemistry, computer science, and information technology for predictive modeling.
D. Olawade, Oluwaseun Fapohunda, S. O. Usman, Abiola D. Akintayo, A. Ige, Yemi A. Adekunle, A. Adeola. Artificial Intelligence in Computational and Materials Chemistry: Prospects and Limitations. 2025. https://doi.org/10.1007/s42250-025-01343-8
Highlights computational chemistry as an intersection of theoretical chemistry and computer science using artificial intelligence.
Kuzma Khrabrov, Anton Ber, A. Tsypin, Konstantin Ushenin, Egor Rumiantsev, Alexander Telepov, Dmitry Protasov, I. Shenbin, Anton M. Alekseev, M. Shirokikh, Sergey I. Nikolenko, Elena Tutubalina, Artur Kadurin. ∇2DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials. 2024. https://doi.org/10.48550/arXiv.2406.14347
Presents neural network potentials as a computational alternative to quantum chemistry methods for predicting molecular properties.
Pooja Gupta, Arsh Chanana, Yukta R. Kulkarni, Aditya Narayan, Ujwal Havelikar, Oma Shanker, Bhabesh Mahato, Dharmendra Singh, Akhilesh Patel, Ravindra Pal Singh, H. Chawra. Computer-aided Drug Design: Innovation and its Application in Reshaping Modern Medicine. 2024. https://doi.org/10.2174/0129503752321279241126091807
Identifies computer-aided drug design as a multidisciplinary field at the interaction of chemistry and computational science.
Meiying Qin, H. Kouyoumdjian, Jonatan Schroeder, Larry Yueli Zhang, Jade Atallah. Contextual Learning in CS1: Integrating a Chemistry Project to Reinforce Core Programming Concepts. 2025. https://doi.org/10.1145/3724389.3731272
Describes teaching cheminformatics in an undergraduate course as an application of computational methods to chemical data.
S. Jamal, Sonam Arora, V. Scaria. Computational Analysis and Predictive Cheminformatics Modeling of Small Molecule Inhibitors of Epigenetic Modifiers. 2016. https://doi.org/10.1371/journal.pone.0083032
Applies machine learning algorithms and computational predictive models to small molecule inhibitors in chemistry.
Banerjee A, Kumar V, Das S, Bhattacharyya P, Ojha PK, Dasgupta I, Gayen S, Chandra Y, Roy PP, Yang S, Li L, Kar S, Bhat-Ambure J, Ambure P, Roy K. A round-robin exercise for the precise prediction of aqueous solubility of organic chemicals using chemometric, machine learning, and stacking ensemble of deep learning models.. 2026. https://doi.org/10.1007/s10822-026-00854-x
Employs chemometric, machine learning, and deep learning models to predict the aqueous solubility of organic chemicals.
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