A Comprehensive Computational Framework for Materials Design, Ab Initio Modeling and Molecular Docking

To facilitate rational molecular and materials design, this research proposes an integrated computational framework that combines stochastic simulation, ab initio quantum chemistry, and molecular docking. The suggested workflow allows systematic investigation of structural stability, binding affinity, and electronic properties across biological and materials science domains by utilizing complementary tools like Avogadro for molecular construction and visualization, AutoDock for docking and interaction analysis, and ORCA for high-level electronic structure computations. Uncertainty, configurational sampling, and optimization in high-dimensional chemical spaces are addressed by combining Monte Carlo-based and annealing-inspired techniques. The work shows how materials science ideas such as polymer design, thin films, crystalline lattices, and bioelectronic systems can be applied to drug development. On-device, open-source computational methods are viable, scalable, and economical, as demonstrated by comparative platform analysis. All things considered, the findings highlight the need of an integrated, repeatable computational pipeline for speeding up de novo molecule assembly and materials architecture while lowering experimental risk and expense.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-12-31
DOI
https://doi.org/10.5281/zenodo.18333474
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
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article

A Comprehensive Computational Framework for Materials Design, Ab Initio Modeling and Molecular Docking

Rakibul Karim Akanda, Michael P. Richard
Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Materials Science
article

A Comprehensive Computational Framework for Materials Design, Ab Initio Modeling and Molecular Docking

Rakibul Karim Akanda, Michael P. Richard
article en

Abstract

To facilitate rational molecular and materials design, this research proposes an integrated computational framework that combines stochastic simulation, ab initio quantum chemistry, and molecular docking. The suggested workflow allows systematic investigation of structural stability, binding affinity, and electronic properties across biological and materials science domains by utilizing complementary tools like Avogadro for molecular construction and visualization, AutoDock for docking and interaction analysis, and ORCA for high-level electronic structure computations. Uncertainty, configurational sampling, and optimization in high-dimensional chemical spaces are addressed by combining Monte Carlo-based and annealing-inspired techniques. The work shows how materials science ideas such as polymer design, thin films, crystalline lattices, and bioelectronic systems can be applied to drug development. On-device, open-source computational methods are viable, scalable, and economical, as demonstrated by comparative platform analysis. All things considered, the findings highlight the need of an integrated, repeatable computational pipeline for speeding up de novo molecule assembly and materials architecture while lowering experimental risk and expense.

Zenodo (CERN European Organization for Nuclear Research)
Savannah State University (US)
Openalex Percentile: Top 46%
Machine Learning in Materials Science
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