AIMC Topic: Quantitative Structure-Activity Relationship
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Advanced Mass-Spectra-Based Machine Learning for Predicting the Toxicity of Traditional Chinese Medicines.
Analytical chemistry
Dec 20, 2024
Traditional Chinese medicine (TCM) has been a cornerstone of health care for centuries, valued for its preventive and therapeutic properties. However, recent decades have revealed significant toxicological concerns associated with TCMs due to their c...
AI-based classification of anticancer drugs reveals nucleolar condensation as a predictor of immunogenicity.
Molecular cancer
Dec 20, 2024
BACKGROUND: Immunogenic cell death (ICD) inducers are often identified in phenotypic screening campaigns by the release or surface exposure of various danger-associated molecular patterns (DAMPs) from malignant cells. This study aimed to streamline t...
Enhanced prediction of beta-secretase inhibitory compounds with mol2vec technique and machine learning algorithms.
SAR and QSAR in environmental research
Dec 20, 2024
A comprehensive computational strategy that combined QSAR modelling, molecular docking, and ADMET analysis was used to discover potential inhibitors for β-secretase 1 (BACE-1). A dataset of 1,138 compounds with established BACE-1 inhibitory activitie...
Cyto-Safe: A Machine Learning Tool for Early Identification of Cytotoxic Compounds in Drug Discovery.
Journal of chemical information and modeling
Dec 11, 2024
Cytotoxicity is essential in drug discovery, enabling early evaluation of toxic compounds during screenings to minimize toxicological risks. assays support high-throughput screening, allowing for efficient detection of toxic substances while conside...
Hierarchical Graph Attention Network with Positive and Negative Attentions for Improved Interpretability: ISA-PN.
Journal of chemical information and modeling
Dec 9, 2024
With the advancement of deep learning (DL) methods in chemistry and materials science, the interpretability of DL models has become a critical issue in elucidating quantitative (molecular) structure-property relationships. Although attention mechanis...
Natural compounds for Alzheimer's prevention and treatment: Integrating SELFormer-based computational screening with experimental validation.
Computers in biology and medicine
Dec 9, 2024
BACKGROUND: This study aimed to develop and apply a novel computational pipeline combining SELFormer, a transformer architecture-based chemical language model, with advanced deep learning techniques to predict natural compounds (NCs) with potential i...
FormulationBCS: A Machine Learning Platform Based on Diverse Molecular Representations for Biopharmaceutical Classification System (BCS) Class Prediction.
Molecular pharmaceutics
Dec 8, 2024
The Biopharmaceutics Classification System (BCS) has facilitated biowaivers and played a significant role in enhancing drug regulation and development efficiency. However, the productivity of measuring the key discriminative properties of BCS, solubi...
ToxSTK: A multi-target toxicity assessment utilizing molecular structure and stacking ensemble learning.
Computers in biology and medicine
Dec 6, 2024
Drug registration requires risk assessment of new active pharmaceutical ingredients or excipients to ensure they are safe for human health and the environment. However, traditional risk assessment is expensive and relies heavily on animal testing. Ma...
A deep learning model based on the BERT pre-trained model to predict the antiproliferative activity of anti-cancer chemical compounds.
SAR and QSAR in environmental research
Nov 28, 2024
Identifying new compounds with minimal side effects to enhance patients' quality of life is the ultimate goal of drug discovery. Due to the expensive and time-consuming nature of experimental investigations and the scarcity of data in traditional QSA...
A comprehensive machine learning-based models for predicting mixture toxicity of azole fungicides toward algae (Auxenochlorella pyrenoidosa).
Environment international
Nov 23, 2024
Quantitative structure-activity relationships (QSARs) have been used to predict mixture toxicity. However, current research faces gaps in achieving accurate predictions of the mixture toxicity of azole fungicides. To address this gap, the application...
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