Faculty Publications

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Scholarly Publications by Integral Academia

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    Computational and artificial intelligence methodologies for big data analytics in structural biology
    (Academic Press, 2026) Mohammad Kalim Ahmad Khan , Umme Aiman, Salman Akhtar
    High-throughput technologies have rapidly advanced, transforming structural biology and bioinformatics into data-intensive areas. Next-generation sequencing, cryo-electron microscopy (cryo-EM), high-resolution mass spectrometry, and large-scale interactomes generate unprecedented amounts of genomic, transcriptomic, proteomic, structural, and imaging data. This exponential growth of biological data poses significant computational and analytical hurdles, necessitating robust frameworks capable of integrating diverse data sources, minimizing noise, ensuring reproducibility, and providing biologically valuable insights. Protein structure prediction, drug discovery, functional annotation, and system-level studies have all been revolutionized by the emergence of machine learning, deep learning, and sophisticated statistical modeling as effective methods for handling this complexity. Despite these gains, many obstacles remain, including computing cost, data quality variance, algorithmic transparency, and concerns about data privacy and security concerns. This chapter reviews the major sources of biological big data, their defining characteristics, and the computational methodologies employed for their analysis, while surveying key applications across structural biology and bioinformatics. Special emphasis is placed on artificial intelligence (AI)-driven structure prediction, cryo-EM data processing, integrative modeling, multiomics analytics, and their growing impact on drug discovery and precision medicine. Emerging trends, such as explainable AI, quantum-enabled computation, and federated learning, are also discussed for their potential to enable interpretable, scalable, and privacy-preserving analytical frameworks. The synergy between big data analytics and experimental technologies will continue to speed up discoveries by enhancing mechanistic knowledge and facilitating translational advancements as it becomes an essential component of contemporary biological research.
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    Novel drug design and bioinformatics: an introduction
    (De Gruyter, 2021) Mohammad Kalim Ahmad Khan ,Salman Akhtar
    In the current era of high-throughput technology, where enormous amounts of biological data are generated day by day via various sequencing projects, thereby the staggering volume of biological targets deciphered. The discovery of new chemical entities and bioisosteres of relatively low molecular weight has been gaining high momentum in the pharmacopoeia, and traditional combinatorial design wherein chemical structure is used as an initial template for enhancing efficacy pharmacokinetic selectivity properties. Once the compound is identified, it undergoes ADMET filtration to ensure whether it has toxic and mutagenic properties or not. If the compound has no toxicity and mutagenicity is either considered a potential lead molecule. Understanding the mechanism of lead molecules with various biological targets is imperative to advance related functions for drug discovery and development. Notwithstanding, a tedious and costly process, taking around 10–15 years and costing around $4 billion, cascaded approached of Bioinformatics and Computational biology viz., structure-based drug design (SBDD) and cognate ligand-based drug design (LBDD) respectively rely on the availability of 3D structure of target biomacromolecules and vice versa has made this process easy and approachable. SBDD encompasses homology modelling, ligand docking, fragment-based drug design and molecular dynamics, while LBDD deals with pharmacophore mapping, QSAR, and similarity search. All the computational methods discussed herein, whether for target identification or novel ligand discovery, continuously evolve and facilitate cost-effective and reliable outcomes in an era of overwhelming data.