Deep Learning and Computational Chemistry

James, Tim; Hristozov, Dimitar

doi:10.1007/978-1-0716-1787-8_5

Cited by 4 publications

(2 citation statements)

References 88 publications

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“…Although various machine learning techniques were explored, including deep learning methods (131)(132)(133)(134)(135)(136), the findings indicate that the performance of deep neural networks does not surpass that of the RF model. The limited success observed in our studies with deep neural networks can often be attributed to insufficient data in the training set.…”

Section: The Data Driven Tight Binding Model For Biomoleculesmentioning

confidence: 99%

Elucidating Electronic Structure Variations in Nucleic Acid-Protein Complexes Involved in Transcription Regulation Using a Tight-Binding Approach

Du,

Liu

2024

Preprint

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Transcription factor (TF) are proteins that regulates the transcription of genetic information from DNA to messenger RNA by binding to a specific DNA sequence. Nucleic acid-protein interactions are crucial in regulating transcription in biological systems. This work presents a quick and convenient method for constructing tight-binding models and offers physical insights into the electronic structure properties of transcription factor complexes and DNA motifs. The tight binding Hamiltonian parameters are generated using the random forest regression algorithm, which reproduces the given ab-initio level calculations with reasonable accuracy. We present a library of residue-level parameters derived from extensive electronic structure calculations over various possible combinations of nucleobases and amino acid side chains from high-quality DNA-protein complex structures. As an example, our approach can reasonably generate the subtle electronic structure details for the orthologous transcription factors human AP-1 and Epstein-Barr virus Zta within a few seconds on a laptop. This method potentially enhances our understanding of the electronic structure variations of gene-protein interaction complexes, even those involving dozens of proteins and genes. We hope this study offers a powerful tool for analyzing transcription regulation mechanisms at an electronic structural level.

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Section: The Data Driven Tight Binding Model For Biomoleculesmentioning

confidence: 99%

Elucidating Electronic Structure Variations in Nucleic Acid-Protein Complexes Involved in Transcription Regulation Using a Tight-Binding Approach

Du,

Liu

2024

Preprint

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“…While AI and machine learning have already entrenched themselves in CADD, the proliferation of deep learning models promises even more precise predictions. These models, trained on vast datasets, might eventually surpass traditional simulation methods in accuracy [ 144 ]. With advances in biology, previously deemed ‘undruggable’ targets are now within CADDs crosshairs.…”

Section: The Future Outlook: Cadds Trajectory and Upcoming Challengesmentioning

confidence: 99%

Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis

Niazi,

Mariam

2023

Pharmaceuticals

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In the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADDs historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting drug discovery. As CADD advances, incorporating diverse biological data and ensuring data privacy become paramount. Challenges persist, demanding the optimization of algorithms and robust ethical frameworks. Integrating Machine Learning and Artificial Intelligence amplifies CADDs predictive capabilities, yet ethical considerations and scalability challenges linger. Collaborative efforts and global initiatives, exemplified by platforms like Open-Source Malaria, underscore the democratization of drug discovery. The convergence of CADD with personalized medicine offers tailored therapeutic solutions, though ethical dilemmas and accessibility concerns must be navigated. Emerging technologies like quantum computing, immersive technologies, and green chemistry promise to redefine the future of CADD. The trajectory of CADD, marked by rapid advancements, anticipates challenges in ensuring accuracy, addressing biases in AI, and incorporating sustainability metrics. This paper concludes by highlighting the need for proactive measures in navigating the ethical, technological, and educational frontiers of CADD to shape a healthier, brighter future in drug discovery.

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Explainable artificial intelligence: A taxonomy and guidelines for its application to drug discovery

Ponzoni

Prosper²,

Campillo

2023

WIREs Comput Mol Sci

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Artificial intelligence (AI) is having a growing impact in many areas related to drug discovery. However, it is still critical for their adoption by the medicinal chemistry community to achieve models that, in addition to achieving high performance in their predictions, can be trusty explained to the end users in terms of their knowledge and background. Therefore, the investigation and development of explainable artificial intelligence (XAI) methods have become a key topic to address this challenge. For this reason, a comprehensive literature review about explanation methodologies for AI based models, focused in the field of drug discovery, is provided. In particular, an intuitive overview about each family of XAI approaches, such as those based on feature attribution, graph topologies, or counterfactual reasoning, oriented to a wide audience without a strong background in the AI discipline is introduced. As the main contribution, we propose a new taxonomy of the current XAI methods, which take into account specific issues related with the typical representations and computational problems study in the design of molecules. Additionally, we also present the main visualization strategies designed for supporting XAI approaches in the chemical domain. We conclude with key ideas about each method category, thoroughly providing insightful analysis about the guidelines and potential benefits of their adoption in medical chemistry.This article is categorized under: Data Science > Artificial Intelligence/Machine Learning

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Deep Learning and Computational Chemistry

Cited by 4 publications

References 88 publications

Elucidating Electronic Structure Variations in Nucleic Acid-Protein Complexes Involved in Transcription Regulation Using a Tight-Binding Approach

Elucidating Electronic Structure Variations in Nucleic Acid-Protein Complexes Involved in Transcription Regulation Using a Tight-Binding Approach

Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis

Explainable artificial intelligence: A taxonomy and guidelines for its application to drug discovery

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