Current pharmaceutical design · 2026
Identification of Lead Compounds for Pesticides using Computer Aided Drug Discovery.
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Aslam Fatima, Razzaq Abdul, Dawood Hussain, Qadri Salman, Khan Zulqurnain, Aziz-Ur-Rehman Muhammad
Аннотация
INTRODUCTION: The agricultural sector heavily relies on pesticides to mitigate the impact of pest infestations. In recent years, Computer-Aided Drug Discovery (CADD) has become popular in the pharmaceutical industry for its cost and time efficiency. The proposed research uses CADD methodologies to identify lead compounds for pesticide development.
METHODS: This study focuses on pharmacophore properties and Chemically Advanced Template Search (CATS) descriptors to assess the similarity score against known pesticides. Similarity metrics, including the Tanimoto coefficient and Cosine similarity, were used to ensure precise molecular similarity. The Analysis of Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADME-T) properties, along with physicochemical characteristics, enhances the understanding of newly identified lead compounds. Furthermore, the Hao filter was applied to physicochemical properties to screen pesticide-like compounds. The Hao filter criteria included molecular weight (≤ 435), number of rotatable bonds (≤9), number of hydrogen bond donors (≤2), number of hydrogen bond acceptors (≤ 6), and logP (≤ 6).
RESULTS: Subsequently, from the initial pool of 120 lead compounds, 75 compounds were identified as meeting the criteria of the Hao filter, demonstrating their potential as promising pesticide candidates.
DISCUSSION: This study underscores the utility of CADD in facilitating the identification and prioritization of potential pesticide candidates.
CONCLUSION: The findings provide insights into the integration of computational approaches in agrochemical discovery.
METHODS: This study focuses on pharmacophore properties and Chemically Advanced Template Search (CATS) descriptors to assess the similarity score against known pesticides. Similarity metrics, including the Tanimoto coefficient and Cosine similarity, were used to ensure precise molecular similarity. The Analysis of Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADME-T) properties, along with physicochemical characteristics, enhances the understanding of newly identified lead compounds. Furthermore, the Hao filter was applied to physicochemical properties to screen pesticide-like compounds. The Hao filter criteria included molecular weight (≤ 435), number of rotatable bonds (≤9), number of hydrogen bond donors (≤2), number of hydrogen bond acceptors (≤ 6), and logP (≤ 6).
RESULTS: Subsequently, from the initial pool of 120 lead compounds, 75 compounds were identified as meeting the criteria of the Hao filter, demonstrating their potential as promising pesticide candidates.
DISCUSSION: This study underscores the utility of CADD in facilitating the identification and prioritization of potential pesticide candidates.
CONCLUSION: The findings provide insights into the integration of computational approaches in agrochemical discovery.