Insights into the in-silico research: Current scenario, advantages, limits, and future perspectives

Abstract views: 1284 / PDF downloads: 692



Theoretical analysis, In silico, Docking, Molecular dynamics


In silico refers to tests or simulations carried out on a computer or by theoretical analysis, as opposed to "in vivo" (in a real organism) or "in vitro" (in a laboratory). This sort of study has applications in many domains, including drug discovery and development, genetics, and bioinformatics. Many benefits of using in silico methods include the capacity to run tests more rapidly and at a lower cost, as well as test ideas that would be difficult or impossible to test in vivo or in vitro. However, it should be noted that the outcomes of in silico trials should be confirmed by experimental or observational investigations. In silico research is predicted to play an important role in many fields of study in the future, providing vital insights and allowing researchers to solve complex challenges and make educated decisions. This work provides an overview of in silico research, including the current scenario, advantages, several applications, limitations, and future perspectives.


Aijaz, M., Keserwani, N., Yusuf, M., Ansari, N. H., Ushal, R., & Kalia, P. (2022). Chemical, biological, and pharmacological prospects of caffeic acid. Biointerface Research in Applied Chemistry, 13(4), 324.

Alberca, L. N., Chuguransky, S. R., Álvarez, C. L., Talevi, A., & Salas-Sarduy, E. (2019). In silico guided drug repurposing: discovery of new competitive and non-competitive inhibitors of falcipain-2. Frontiers in Chemistry, 7, 534.

Almehmadi, M. M., Halawi, M., Kamal, M., Yusuf, M., Chawla, U., & Asif, M. (2022). Antimycobacterial Activity of Some New Pyridinylpyridazine Derivatives. Latin American Journal of Pharmacy, 41(7), 1428-1432.

Awale, M., & Reymond, J. L. (2018). Polypharmacology browser PPB2: target prediction combining nearest neighbors with machine learning. Journal of Chemical Information and Modeling, 59(1), 10-17.

Blaschke, T., Arús-Pous, J., Chen, H., Margreitter, C., Tyrchan, C., Engkvist, O., Papadopoulos, K., & Patronov, A. (2020). REINVENT 2.0: An AI tool for de novo drug design. Journal of Chemical Information and Modeling, 60(12), 5918-5922.

Burki, T. (2020). A new paradigm for drug development. The Lancet Digital Health, 2(5), e226-e227.

Caballero, J. (2021). The latest automated docking technologies for novel drug discovery. Expert Opinion on Drug Discovery, 16(6), 625-645.

Chandel, V., Sharma, P. P., Raj, S., Choudhari, R., Rathi, B., & Kumar, D. (2022). Structure-based drug repurposing for targeting Nsp9 replicase and spike proteins of severe acute respiratory syndrome coronavirus 2. Journal of Biomolecular Structure and Dynamics, 40(1), 249-262.

Cho, A. (2020). No room for error. Science, 369, 130-133.

Coley, C. W., Barzilay, R., Green, W. H., Jaakkola, T. S., & Jensen, K. F. (2017). Convolutional embedding of attributed molecular graphs for physical property prediction. Journal of Chemical Information and Modeling, 57(8), 1757-1772.

Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: management, analysis and future prospects. Journal of Big Data, 6(1), 1-25.

Durrant, J. D., & McCammon, J. A. (2011). NNScore 2.0: a neural-network receptor–ligand scoring function. Journal of Chemical Information and Modeling, 51(11), 2897-2903.

Feinberg, E. N., Sur, D., Wu, Z., Husic, B. E., Mai, H., Li, Y., Sun, S., Yang, J., Ramsundar, B., et al. (2018). PotentialNet for molecular property prediction. ACS Central Science, 4(11), 1520-1530.

Ghasemi, F., Mehridehnavi, A., Fassihi, A., & Pérez-Sánchez, H. (2018). Deep neural network in QSAR studies using deep belief network. Applied Soft Computing, 62, 251-258.

Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., et al. (2018). Automatic chemical design using a data-driven continuous representation of molecules. ACS Central Science, 4(2), 268-276.

González-Medina, M., Naveja, J. J., Sánchez-Cruz, N., & Medina-Franco, J. L. (2017). Open chemoinformatic resources to explore the structure, properties and chemical space of molecules. RSC Advances, 7(85), 54153-54163.

Grelck, C., Niewiadomska-Szynkiewicz, E., Aldinucci, M., Bracciali, A., & Larsson, E. (2019). Why high-performance modelling and simulation for big data applications matters. In J. Kołodziej & H. González-Vélez (Eds.), High-Performance Modelling and Simulation for Big Data Applications: Selected Results of the COST Action IC1406 cHiPSet (pp. 1-35): Springer International Publishing Cham.

Haensel, M., Schmitt, T. M., & Bogenreuther, J. (2023). Teaching the Modeling of Human–Environment Systems: Acknowledging Complexity with an Agent-Based Model. Journal of Science Education and Technology, 32(2), 256-266.

Kalyane, D., Sanap, G., Paul, D., Shenoy, S., Anup, N., Polaka, S., Tambe, V., & Tekade, R. K. (2020). Artificial intelligence in the pharmaceutical sector: current scene and future prospect. In R. K. Tekade (Ed.), The future of pharmaceutical product development and research (pp. 73-107): Elsevier.

Khan, M. A., Iqbal, J., Ilyas, M., Ayub, A. R., Zhu, Y., & Li, H. (2022). Controlled supramolecular interaction to enhance the bioavailability of hesperetin to targeted cancer cells through graphyne: a comprehensive in silico study. RSC Advances, 12(10), 6336-6346.

Khanal, P., Chawla, U., Praveen, S., Malik, Z., Malik, S., Yusuf, M., Khan, S. A., & Sharma, M. (2021). Study of naturally-derived biomolecules as therapeutics against SARS-CoV-2 viral spike protein. Journal of Pharmaceutical Research International, 33(28A), 211-220.

Kumar, D., Chaudhary, J., Kumar, S., Bhardwaj, S., Yusuf, M., & Verma, A. (2021). Investigation of methylammonium lead bromide hybrid perovskite based photoactive material for the photovoltaic applications. Digest Journal of Nanomaterials & Biostructures (DJNB), 16(1), 205-215.

Kurya, A. U., Aliyu, U., Tudu, A. I., Usman, A., Yusuf, M., Gupta, S., Ali, A., Gulfishan, M., Singh, S. K., et al. (2022). Graft-versus-host disease: therapeutic prospects of improving the long-term post-transplant outcomes. Transplantation Reports, 7(4), 100107.

Lugli, G. A., Milani, C., Mancabelli, L., Turroni, F., van Sinderen, D., & Ventura, M. (2019). A microbiome reality check: limitations of in silico‐based metagenomic approaches to study complex bacterial communities. Environmental Microbiology Reports, 11(6), 840-847.

Madariaga-Mazón, A., Naveja, J. J., Medina-Franco, J. L., Noriega-Colima, K. O., & Martinez-Mayorga, K. (2021). DiaNat-DB: a molecular database of antidiabetic compounds from medicinal plants. RSC Advances, 11(9), 5172-5178.

Mak, K. K., & Pichika, M. R. (2019). Artificial intelligence in drug development: present status and future prospects. Drug Discovery Today, 24(3), 773-780.

Martin, R. L., Iraola, D. M., Louie, E., Pierce, D., Tagtow, B., Labrie, J. J., & Abrahamson, P. G. (2018). Hybrid natural language processing for high-performance patent and literature mining in IBM Watson for Drug Discovery. IBM Journal of Research and Development, 62(6), 1-12.

Mayr, A., Klambauer, G., Unterthiner, T., & Hochreiter, S. (2016). DeepTox: Toxicity prediction using deep learning. Frontiers in Environmental Science, 3, 80.

Mocci, F., de Villiers Engelbrecht, L., Olla, C., Cappai, A., Casula, M. F., Melis, C., Stagi, L., Laaksonen, A., & Carbonaro, C. M. (2022). Carbon nanodots from an in silico perspective. Chemical Reviews, 122(16), 13709-13799.

Naveja, J. J., Dueñas-González, A., & Medina-Franco, J. L. (2016). Drug repurposing for epigenetic targets guided by computational methods. In J. L. Medina-Franco (Ed.), Epi-informatics (pp. 327-357): Elsevier.

Naveja, J. J., Vogt, M., Stumpfe, D., Medina-Franco, J. L., & Bajorath, J. (2019). Systematic extraction of analogue series from large compound collections using a new computational compound–core relationship method. ACS Omega, 4(1), 1027-1032.

Novick, P. A., Ortiz, O. F., Poelman, J., Abdulhay, A. Y., & Pande, V. S. (2013). SWEETLEAD: An in silico database of approved drugs, regulated chemicals, and herbal isolates for computer-aided drug discovery. PloS One, 8(11), e79568.

Preuer, K., Lewis, R. P., Hochreiter, S., Bender, A., Bulusu, K. C., & Klambauer, G. (2018). DeepSynergy: predicting anti-cancer drug synergy with Deep Learning. Bioinformatics, 34(9), 1538-1546.

Ramesh, A., Kambhampati, C., Monson, J. R., & Drew, P. (2004). Artificial intelligence in medicine. Annals of the Royal College of Surgeons of England, 86(5), 334-338.

Sanchez-Lengeling, B., Outeiral, C., Guimaraes, G. L., & Aspuru-Guzik, A. (2017). Optimizing distributions over molecular space. An objective-reinforced generative adversarial network for inverse-design chemistry (ORGANIC). ChemRxiv. Cambridge: Cambridge Open Engage.

Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W., et al. (2020). Improved protein structure prediction using potentials from deep learning. Nature, 577(7792), 706-710.

Sharma, V. K., Yusuf, M., Kumar, P., Sharma, M., Waila, V. C., Khan, S. A., Ansari, N. H., & Zaphar, S. (2021). A Review of the State of the Art towards Biological Applications of Graphene-based Nanomaterials. Journal of Pharmaceutical Research International, 33(55B), 216-230.

Siafaka, P. I., Bulbul, E. O., Miliotou, A. N., Karantas, I. D., Okur, M. E., & Okur, N. Ü. (2023). Delivering active molecules to the eye; the concept of electrospinning as potent tool for drug delivery systems. Journal of Drug Delivery Science and Technology, 84, 104565.

Singh, Y. H., Gromiha, M. M., Sarai, A., & Ahmad, S. (2006). Atom-wise statistics and prediction of solvent accessibility in proteins. Biophysical Chemistry, 124(2), 145-154.

Sirous, H., Chemi, G., Gemma, S., Butini, S., Debyser, Z., Christ, F., Saghaie, L., Brogi, S., Fassihi, A., et al. (2019). Identification of novel 3-hydroxy-pyran-4-one derivatives as potent HIV-1 integrase inhibitors using in silico structure-based combinatorial library design approach. Frontiers in Chemistry, 7, 574.

Steiner, S., Wolf, J., Glatzel, S., Andreou, A., Granda, J. M., Keenan, G., Hinkley, T., Aragon-Camarasa, G., Kitson, P. J., et al. (2019). Organic synthesis in a modular robotic system driven by a chemical programming language. Science, 363(6423), eaav2211.

Stork, C., Chen, Y., Sicho, M., & Kirchmair, J. (2019). Hit Dexter 2.0: machine-learning models for the prediction of frequent hitters. Journal of Chemical Information and Modeling, 59(3), 1030-1043.

Tripathi, M. K., Nath, A., Singh, T. P., Ethayathulla, A., & Kaur, P. (2021). Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery. Molecular Diversity, 25, 1439-1460.

Urban, G., Subrahmanya, N., & Baldi, P. (2018). Inner and outer recursive neural networks for chemoinformatics applications. Journal of Chemical Information and Modeling, 58(2), 207-211.

Wang, C., & Zhang, Y. (2017). Improving scoring‐docking‐screening powers of protein–ligand scoring functions using random forest. Journal of Computational Chemistry, 38(3), 169-177.

Wójcikowski, M., Zielenkiewicz, P., & Siedlecki, P. (2015). Open Drug Discovery Toolkit (ODDT): a new open-source player in the drug discovery field. Journal of Cheminformatics, 7(1), 1-6.

Yusuf, M. (2022). Modern Perspectives of Curcumin and Its Derivatives as Promising Bioactive and Pharmaceutical Agents. Biointerface Research in Applied Chemistry, 12, 7177-7204.

Yusuf, M., Aijaz, M., Keserwani, N., Ansari, N. H., & Ahmad, S. (2022). Ethnomedicinal, Pharmacological and Commercial Perspectives of Laccifer lacca Body Exudate (LBE). Letters in Applied NanoBioScience, 12, 1-10.

Yusuf, M., Chawla, U., Ansari, N. H., Sharma, M., & Asif, M. (2023a). Perspective on Metal-Ligand Coordination Complexes and Improvement of Current Drugs for Neurodegenerative Diseases (NDDs). Advanced Journal of Chemistry-Section A, 6(1), 31-49.

Yusuf, M., & Khan, S. A. (2022). Assessment of ADME and in silico Characteristics of Natural-Drugs from Turmeric to Evaluate Significant COX2 Inhibition. Biointerface Research in Applied Chemistry, 13(1), 5-23.

Yusuf, M., Rani, S., Chawla, U., Baindara, P., Siddique, S. A., Nirala, K., & Asif, M. (2023b). Modern Perspectives on Adiponectin: Targeting Obesity, Diabetes, and Cancer Together Using Herbal Products. Biointerface Research in Applied Chemistry, 13(2), 136.

Zhu, H. (2020). Big data and artificial intelligence modeling for drug discovery. Annual Review of Pharmacology and Toxicology, 60, 573-589.




How to Cite

Yusuf, M. (2023). Insights into the in-silico research: Current scenario, advantages, limits, and future perspectives. Life in Silico, 1(1), 13–25. Retrieved from