Author(s)

Md Majedur Rahman¹, Bismi Jatil Alia Juie², Nadia Terasa Tisha³, Ahmed Tanvir^4*

Affiliation(s)

¹Medical Services Department, Incepta Pharmaceuticals Limited, Dhaka 1208, Bangladesh.

²Training Department, Incepta Pharmaceuticals Limited, Dhaka 1208, Bangladesh.

³Faculty of Business Administration, Dhaka University, Dhaka 1000, Bangladesh.

⁴Department of Pharmacology, Ajou University School of Medicine, Suwon 16499, Korea.

Corresponding Author

Ahmed Tanvir

ABSTRACT

The integration of big data, machine learning (ML), and artificial intelligence (AI) is driving a transformative shift in biomedical and ecological sciences. This review examines the pivotal role of predictive analytics across three interconnected fields: drug discovery, disease surveillance, and mycology. In pharmaceutical research, predictive models have significantly accelerated the identification of drug candidates, streamlined lead optimization, and enhanced toxicity prediction ultimately reducing both time and cost. Advanced approaches, including deep learning and graph-based algorithms, are now standard tools for designing new therapeutics and efficiently screening compound libraries. While mycology has traditionally been underrepresented in computational research, it is now benefiting from predictive analytics through improved fungal classification, ecological modeling, and biosurveillance. Progress in image recognition and genomic trait prediction is opening new frontiers for studying fungal biodiversity and discovering bioactive compounds. Despite these advances, critical challenges remain, such as data heterogeneity, limited model interpretability, regulatory hurdles, and ethical concerns. This review identifies these barriers and proposes strategic solutions, including the integration of multimodal datasets, increased model transparency, and broader accessibility of analytical tools. By bridging innovations in drug development, public health, and fungal science, this review underscores the growing synergy between predictive analytics and life sciences offering a pathway to faster drug development, enhanced disease diagnostics, and more informed ecosystem management.

KEYWORDS

Predictive analytics; Drug discovery; Fungal biodiversity; Machine learning integration

CITE THIS PAPER

Md Majedur Rahman, Bismi Jatil Alia Juie, Nadia Terasa Tisha, Ahmed Tanvir. Harnessing predictive analytics and machine learning in drug discovery, disease surveillance, and fungal research. Eurasia Journal of Science and Technology. 2022, 4(2): 28-35. DOI: https://doi.org/10.61784/ejst3099.

REFERENCES

[1] Adans-Dester C, Bamberg S, Bertacchi F, et al. Can mHealth Technology Help Mitigate the Effects of the COVID-19 Pandemic? IEEE Open Journal of Engineering in Medicine and Biology, 2020, 1: 243.

[2] Abdullahi AA. Trends and challenges of traditional medicine in Africa. African Journal of Traditional, Complementary and Alternative Medicines, 2011, 8: 115–123.

[3] Aerts J. Special Issue on “Human Health Engineering”. Applied Sciences, 2020, 10(2): 564.

[4] Allegra M. Antioxidant and anti-inflammatory properties of plants extract. Antioxidants, 2019, 8: 549.

[5] Chaudhary PH, Khadabadi S. Bombax ceiba Linn.: Pharmacognosy, Ethnobotany and Phyto-pharmacology. Pharmacognosy Communications, 2012, 2: 2–9.

[6] Manik M M T G, Bhuiyan M M R, Moniruzzaman M, et al. The Future of Drug Discovery Utilizing Generative AI and Big Data Analytics for Accelerating Pharmaceutical Innovations, Nanotechnology Perceptions, 2018, 14(3): 120-135. DOI: 10.62441/nano-ntp.v14i3.4766.

[7] Marzana A, Aminuzzaman FM, Chowdhury MSM, et al. Diversity and Ecology of Macrofungi in Rangamati of Chittagong Hill Tracts Under Tropical Evergreen and Semi-Evergreen Forest of Bangladesh. Advances in Research, 2018, 13(5): 1–17.

[8] Medina N, Alastruey-Izquierdo A, Mercado D, et al. Diagnostic Mycology Laboratories Should Have a Central Role for the Management of Fungal Disease. Journal of Fungi, 2022, 8: 1285. DOI: 10.3390/jof8121285.

[9] Meyer V, Basenko EY, Benz JP, et al. Growing a circular economy with fungal biotechnology: a white paper. Fungal Biology and Biotechnology, 2020, 7: 5. DOI: 10.1186/s40694-020-00095-z.

[10] Hossain D. A fire protection life safety analysis of multipurpose building. 2021. https://digitalcommons.calpoly.edu/fpe_rpt/135/.

[11] Manik M M T G, Hossain S, Ahmed M K, et al. Integrating Genomic Data and Machine Learning to Advance Precision Oncology and Targeted Cancer Therapies. Nanotechnology Perceptions, 2022, 18(2): 219-243. DOI: 10.62441/nano-ntp.v18i2.5443.

[12] Peyclit L, Yousfi H, Rolain JM, et al. Drug Repurposing in Medical Mycology: Identification of Compounds as Potential Antifungals to Overcome the Emergence of Multidrug-Resistant Fungi. Pharmaceuticals, 2021, 14: 488. DOI: 10.3390/ph14050488.

[13] Miah M A, Rozario E, Khair F B, et al. Harnessing Wearable Health Data and Deep Learning Algorithms for Real-Time Cardiovascular Disease Monitoring and Prevention, Nanotechnology Perceptions, 2019, 15(3): 326-349.  DOI: 10.62441/nano-ntp.v15i3.5278s.

[14] Rosa A Borner, Vijayalakshmi Kandasamy, Amalie M Axelsen, et al. Genome editing of lactic acid bacteria: opportunities for food, feed, pharma and biotech. FEMS Microbiology Letters, 2019, 366(1). DOI: 10.1093/femsle/fny291.

[15] Jonathan M Stokes, Kevin Yang, Kyle Swanson, et al. A Deep Learning Approach to Antibiotic Discovery. Cell, 2020, 180(4): 688-702.DOI: 10.1016/j.cell.2020.01.021.

[16] Hossain D. Fire dynamics and heat transfer: advances in flame spread analysis. Open Access Research Journal of Science and Technology, 2022, 6(2): 70-5. DOI: 10.53022/oarjst.2022.6.2.0061.

[17] Manik M M T G. An Analysis of Cervical Cancer using the Application of AI and Machine Learning. Journal of Medical and Health Studies, 2022, 3(2): 67-76. DOI: 10.32996/jmhs.2022.3.2.11.

[18] Dongmei Li, Xiaodong She, Richard Calderone. The antifungal pipeline: the need is established. Are there new compounds? FEMS Yeast Research, 2020, 20(4). DOI: 10.1093/femsyr/foaa023.

[19] Sitarek P, Merecz-Sadowska A, Kowalczyk T, et al. Potential Synergistic Action of Bioactive Compounds from Plant Extracts against Skin Infecting Microorganisms. International Journal of Molecular Sciences, 2020, 21: 5105. DOI: 10.3390/ijms21145105.

[20] Hassan J, Barikdar C R, Rozario E, et al. Emerging Trends and Performance Evaluation of Eco-Friendly Construction Materials for Sustainable Urban Development. Journal of Mechanical, Civil and Industrial Engineering, 2022, 2(2): 80-90. DOI: 10.32996/jmcie.2021.2.2.11.

[21] Andrew C Liu, Krishna Patel, Ramya Dhatri Vunikili, et al. Sepsis in the era of data-driven medicine: personalizing risks, diagnoses, treatments and prognoses, Briefings in Bioinformatics, 2020, 21(4): 1182–1195, DOI: 10.1093/bib/bbz059.

[22] Ma S, Chou W, Chien T, et al. An App for Detecting Bullying of Nurses Using Convolutional Neural Networks and Web-Based Computerized Adaptive Testing: Development and Usability Study. JMIR mHealth and uHealth, 2020, 8(5): e16747.

[23] Tobore I, Li J, Yuhang L, et al. Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations. JMIR Mhealth Uhealth, 2019, 7(8): e11966.

[24] Lee Y, Chou W, Chien T, et al. An App Developed for Detecting Nurse Burnouts Using the Convolutional Neural Networks in Microsoft Excel: Population-Based Questionnaire Study. JMIR Medical Informatics, 2020, 8(5): e16528.

[25] Bulbul IJ, Zahir Z, Tanvir A, et al. Comparative study of the antimicrobial, minimum inhibitory concentrations (MIC), cytotoxic and antioxidant activity of methanolic extract of different parts of Phyllanthus acidus (l.) Skeels (family: Euphorbiaceae). World Journal of Pharmacy and Pharmaceutical Sciences, 2018, 8(1): 12-57. DOI: 10.20959/wjpps20191-10735.

[26] Aminuzzaman FM, Shahi S, Thapa S, et al. Mushroom Diseases and Their Management: A Review. In: Kulshreshtha S, Ukaogo PO, Siddhant. Recent Advances in Mushroom Cultivation Technology and Its Application. Delhi, India: Bright Sky Publications, 2022: 1–27.

[27] Das K, Aminuzzaman FM, Nasim A. Diversity of Fleshy Macro Fungi in Mangrove Forest Regions of Bangladesh. Journal of Biology and Nature, 2016, 6(4): 218–241.

[28] Das K, Jhan PK, DasSC, et al. Nanotechnology: Past, Present and Future Prospects in Crop Protection. In: Ahmad F, Sultan M. Technology in Agriculture. London, United Kingdom: IntechOpen Limited, 2021: 1–18.

[29] Das K, Aminuzzaman FM. Morphological and Ecological Characterization of Xylotrophic Fungi in Mangrove Forest Regions of Bangladesh. Journal of Advances in Biology & Biotechnology, 2017, 11(4): 1–15.

[30] Rubina H, Aminuzzaman FM, Chowdhury MSM, et al. Morphological Characterization of Macro Fungi Associated with Forest Tree of National Botanical Garden, Dhaka. Journal of Advances in Biology & Biotechnology, 2017, 11(4): 1–18.

[31] Aminuzzaman FM, Das K. Morphological Characterization of Polypore Macro Fungi Associated with Dalbergia Sissoo Collected from Bogra District Under Social Forest Region of Bangladesh. Journal of Biology and Nature, 2017, 6(4): 199–212.

[32] Das K Das SC, Aminuzzaman FM. Environmental and Ecological Impact of Soil Microorganisms in Plant Sciences. Journal of Ecology and Natural Resources, 2022, 6(2): 1–11.

[33] Alasa VK, Alasa K, Karim D. Predictive Analytics in Drug Discovery, Disease Monitoring, and Mycology. International Journal of Multidisciplinary Research and Growth Evaluation, 2022, 3(2): 825–830.