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...
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...
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...
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...
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...
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...
The presence of Activity Cliffs (ACs) has been known to represent a challenge for QSAR modeling. With its high data dependency, Machine Learning QSAR models will be directly influenced by the activity landscape. We propose several extended similarity...
A quantitative structure-activity relationship (QSAR) study was conducted on 313 pesticides to predict their acute toxicity to Sheepshead minnow (Cyprinodon variegatus) by using DRAGON descriptors. Essentials accounting for a reliable model were all ...
IN BRIEF: Clinical drug trials often do not include pregnant people due to health risks; therefore, many medications have an unknown effect on the developing fetus. Machine learning QSAR models have been used successfully to predict the fetal risk of...
Journal of chemical information and modeling
Nov 11, 2024
This study synergizes machine learning (ML) with conceptual density functional theory (CDFT) to develop OECD-compliant predictive models for the mutagenic activity of aromatic amines (AAs) with a fully No-Code methodology using a comprehensive data s...
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