Linear to Neural Networks Regression: QSPR of Drugs via Degree-Distance Indices

• URL: http://arxiv.org/abs/2505.07821v1
• Date: Tue, 18 Mar 2025 20:03:59 GMT
• Title: Linear to Neural Networks Regression: QSPR of Drugs via Degree-Distance Indices
• Authors: M. J. Nadjafi Arani, S. Sorgun, M. Mirzargar,
• Abstract summary: The study provides an innovative perspective on integrating topological indices with machine learning to enhance predictive accuracy.<n>This predictive may also explain that establishing a reliable relationship between topological indices and physical properties enables chemists to gain preliminary insights into molecular behavior.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study conducts a Quantitative Structure Property Relationship (QSPR) analysis to explore the correlation between the physical properties of drug molecules and their topological indices using machine learning techniques. While prior studies in drug design have focused on degree-based topological indices, this work analyzes a dataset of 166 drug molecules by computing degree-distance-based topological indices, incorporating vertex-edge weightings with respect to different six atomic properties (atomic number, atomic radius, atomic mass, density, electronegativity, ionization). Both linear models (Linear Regression, Lasso, and Ridge Regression) and nonlinear approaches (Random Forest, XGBoost, and Neural Networks) were employed to predict molecular properties. The results demonstrate the effectiveness of these indices in predicting specific physicochemical properties and underscore the practical relevance of computational methods in molecular property estimation. The study provides an innovative perspective on integrating topological indices with machine learning to enhance predictive accuracy, highlighting their potential application in drug discovery and development processes. This predictive may also explain that establishing a reliable relationship between topological indices and physical properties enables chemists to gain preliminary insights into molecular behavior before conducting experimental analyses, thereby optimizing resource utilization in cheminformatics research.

Related papers

• Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction [0.6906005491572401]
  Molecular featurisation refers to the transformation of molecular data into numerical feature vectors. Message-passing graph neural networks (GNNs) have emerged as a novel method to learn differentiable features directly from molecular graphs.
  arXiv  Detail & Related papers  (2024-11-20T20:07:48Z)
• Pre-trained Molecular Language Models with Random Functional Group Masking [54.900360309677794]
  We propose a SMILES-based underlineem Molecular underlineem Language underlineem Model, which randomly masking SMILES subsequences corresponding to specific molecular atoms. This technique aims to compel the model to better infer molecular structures and properties, thus enhancing its predictive capabilities.
  arXiv  Detail & Related papers  (2024-11-03T01:56:15Z)
• Stacked ensemble\-based mutagenicity prediction model using multiple modalities with graph attention network [0.9736758288065405]
  Mutagenicity is a concern due to its association with genetic mutations which can result in a variety of negative consequences. In this work, we introduce a novel stacked ensemble based mutagenicity prediction model.
  arXiv  Detail & Related papers  (2024-09-03T09:14:21Z)
• Balancing Molecular Information and Empirical Data in the Prediction of Physico-Chemical Properties [8.649679686652648]
  We propose a general method for combining molecular descriptors with representation learning.<n>The proposed hybrid model exploits chemical structure information using graph neural networks.<n>It automatically detects cases where structure-based predictions are unreliable, in which case it corrects them by representation-learning based predictions.
  arXiv  Detail & Related papers  (2024-06-12T10:51:00Z)
• Unsupervised Learning of Molecular Embeddings for Enhanced Clustering and Emergent Properties for Chemical Compounds [2.6803933204362336]
  We introduce various methods to detect and cluster chemical compounds based on their SMILES data. Our first method, analyzing the graphical structures of chemical compounds using embedding data, employs vector search to meet our threshold value. We also used natural language description embeddings stored in a vector database with GPT3.5, which outperforms the base model.
  arXiv  Detail & Related papers  (2023-10-25T18:00:24Z)
• Towards out-of-distribution generalizable predictions of chemical kinetics properties [61.15970601264632]
  Out-Of-Distribution (OOD) kinetic property prediction is required to be generalizable. In this paper, we categorize the OOD kinetic property prediction into three levels (structure, condition, and mechanism) We create comprehensive datasets to benchmark the state-of-the-art ML approaches for reaction prediction in the OOD setting and the state-of-the-art graph OOD methods in kinetics property prediction problems.
  arXiv  Detail & Related papers  (2023-10-04T20:36:41Z)
• Atomic and Subgraph-aware Bilateral Aggregation for Molecular Representation Learning [57.670845619155195]
  We introduce a new model for molecular representation learning called the Atomic and Subgraph-aware Bilateral Aggregation (ASBA) ASBA addresses the limitations of previous atom-wise and subgraph-wise models by incorporating both types of information. Our method offers a more comprehensive way to learn representations for molecular property prediction and has broad potential in drug and material discovery applications.
  arXiv  Detail & Related papers  (2023-05-22T00:56:00Z)
• Capturing dynamical correlations using implicit neural representations [85.66456606776552]
  We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data. In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
  arXiv  Detail & Related papers  (2023-04-08T07:55:36Z)
• Graph neural networks for the prediction of molecular structure-property relationships [59.11160990637615]
  Graph neural networks (GNNs) are a novel machine learning method that directly work on the molecular graph. GNNs allow to learn properties in an end-to-end fashion, thereby avoiding the need for informative descriptors. We describe the fundamentals of GNNs and demonstrate the application of GNNs via two examples for molecular property prediction.
  arXiv  Detail & Related papers  (2022-07-25T11:30:44Z)
• Low cost prediction of probability distributions of molecular properties for early virtual screening [0.8702432681310399]
  This article applies Hierarchical Correlation Reconstruction approach, previously applied in the analysis of demographic, financial and astronomical data. The whole methodology constitutes therefore a great support for medicinal chemists, as it enable fast rejection of compounds with the lowest potential of desired physicochemical/ADMET characteristic.
  arXiv  Detail & Related papers  (2022-07-21T13:29:26Z)
• Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts [80.69440684790925]
  DeepRelations is a physics-inspired deep relational network with intrinsically explainable architecture. It shows superior interpretability to the state-of-the-art. It boosts the AUPRC of contact prediction 9.5, 16.9, 19.3 and 5.7-fold for the test, compound-unique, protein-unique, and both-unique sets.
  arXiv  Detail & Related papers  (2019-12-29T00:14:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.