Article
Environmental Sciences
R. P. Vivek-Ananth, Ajaya Kumar Sahoo, Shanmuga Priya Baskaran, Janani Ravichandran, Areejit Samal
Summary: Androgen mimicking environmental chemicals can bind to Androgen receptor (AR) and can cause severe effects on male reproductive health. Predicting such endocrine disrupting chemicals (EDCs) in the human exposome is crucial for improving chemical regulations. However, the continuous structure-activity relationship (SAR) doesn't always hold, and activity landscape analysis can help identify unique features such as activity cliffs.
SCIENCE OF THE TOTAL ENVIRONMENT
(2023)
Article
Chemistry, Multidisciplinary
Jiazhen He, Eva Nittinger, Christian Tyrchan, Werngard Czechtizky, Atanas Patronov, Esben Jannik Bjerrum, Ola Engkvist
Summary: Molecular optimization is a fundamental problem in drug discovery, and deep learning methods are proposed to surpass the limited capability of matched molecular pairs (MMPs) and achieve more general structural modifications for improving drug profiles.
JOURNAL OF CHEMINFORMATICS
(2022)
Article
Chemistry, Medicinal
Junhui Park, Gaeun Sung, SeungHyun Lee, SeungHo Kang, ChunKyun Park
Summary: The paper explores the activity cliff phenomenon in drug-target interactions and introduces a graph convolutional network model for predicting activity cliffs, showing superiority in comparison to other methods for popular target datasets. Additionally, the use of gradient-weighted class activation mapping is demonstrated to visualize activation weights at nodes in molecular graphs, potentially aiding in the identification of important substructures for molecular docking.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2022)
Article
Biochemistry & Molecular Biology
Wojciech Pietrus, Rafal Kurczab, Dawid Warszycki, Andrzej J. J. Bojarski, Jurgen Bajorath
Summary: In this study, a total of 898 F-containing isomeric analog sets were identified and analyzed for structure-activity relationship (SAR) in the ChEMBL database. The results showed significant differences in affinity for some isomeric compounds against different aminergic GPCRs, and the change of fluorine position could lead to a significant change in potency. Additionally, a computational workflow was proposed to score the fluorine positions in the molecule.
Article
Biochemistry & Molecular Biology
Velu Shunmuga Priya, Dhinakararajan Pradiba, Murali Aarthy, Sanjeev Kumar Singh, Anant Achary, Mani Vasanthi
Summary: This study identified myricetin as a potent inhibitor of MMP-1, showing its potential to effectively prevent metastasis of breast cancer through computational and experimental validation.
JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS
(2021)
Article
Medicine, Legal
Cathy C. Lester, Gang Yan
Summary: Matched molecular pair analysis (MMPA) offers a systematic method for identifying chemical substitutions to cover the safety of a target chemical. The analysis revealed that only five categories of substitutions per chemical class were necessary to link all molecular pairs, outlining a strategy for searching potential analogs. This approach provides interpretable structural comparisons sensitive to small differences in local structure, in contrast to quantitative similarity measures showing little correlation with analog suitability.
REGULATORY TOXICOLOGY AND PHARMACOLOGY
(2021)
Article
Multidisciplinary Sciences
Alisha N. Jones, Andre Mourao, Anna Czarna, Alex Matsuda, Roberto Fino, Krzysztof Pyrc, Michael Sattler, Grzegorz M. Popowicz
Summary: The replication complex (RC) of SARS-CoV-2 has a fast RNA-dependent RNA polymerase activity, resulting in highly variable genomic sequences. This study characterized the RNA template recognition and elongation fidelity of SARS-CoV-2 RdRp and the role of the exonuclease, nsp14, through biochemical experiments. The results highlight the importance of the 2' OH group in RdRp template recognition and elongation, and suggest the potential use of 3' deoxy-terminator nucleotides as antivirals against SARS-CoV-2.
SCIENTIFIC REPORTS
(2022)
Article
Chemistry, Medicinal
Derek van Tilborg, Alisa Alenicheva, Francesca Grisoni
Summary: Machine learning plays a crucial role in drug discovery and chemistry. However, the effect of activity cliffs - molecules that are structurally similar but exhibit significant differences in potency - on model performance has received limited attention. In this study, we benchmarked 24 machine and deep learning approaches and found that machine learning methods based on molecular descriptors outperformed more complex deep learning methods in predicting the properties of activity cliffs. Our findings highlight the need for dedicated metrics and novel algorithms to address the limitation posed by activity cliffs in molecular machine learning models.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2022)
Article
Chemistry, Multidisciplinary
Chaofeng Lou, Hongbin Yang, Hua Deng, Mengting Huang, Weihua Li, Guixia Liu, Philip W. Lee, Yun Tang
Summary: Chemical mutagenicity is a significant concern in early drug discovery. We developed a well-trained consensus model to reverse mutagenicity using a large data set, which demonstrated the value of these transformation rules for optimizing compounds with mutagenic effects.
JOURNAL OF CHEMINFORMATICS
(2023)
Article
Chemistry, Multidisciplinary
Wasihun Menberu Dagnaw, Ahmed M. Mohammed
Summary: Catalytic hydrogenation is a commonly used reaction type in chemistry and chemical industry. This study computationally explored the potential of using phosphorus(V) and sulfur(VI) as Lewis acid centers to construct metal-free hydrogenation catalysts. It was found that these proposed catalysts can activate hydrogen through a mechanism similar to conventional FLPs.
Article
Immunology
Pengbo Hu, Liang Xu, Yongqing Liu, Xiuyuan Zhang, Zhou Li, Yiming Li, Hong Qiu
Summary: By analyzing the tumor microenvironment of hepatocellular carcinoma using single-cell technology, we constructed molecular typing and risk models for LRs, and investigated their association with prognosis, drug sensitivity, and mutations. We also studied the role of SLC1A5 in liver cancer cells. Our findings provide valuable insights for clinical treatment and prognosis.
FRONTIERS IN IMMUNOLOGY
(2023)
Article
Biochemistry & Molecular Biology
Javed Iqbal, Martin Vogt, Juergen Bajorath
Summary: The study successfully predicted activity cliffs (ACs) by training convolutional neural network (CNN) models with pairs of structural analogs extracted from different compound activity classes. This proof-of-principle demonstrated the capability of CNN models to learn chemistry from images and identify characteristic structural features contributing to successful predictions. Additionally, the CNN models were able to interpret model decisions with intrinsic black box character using weight-based feature visualization.
JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
(2021)
Article
Biochemistry & Molecular Biology
Huabin Hu, Juergen Bajorath
Summary: Very small chemical changes in active compounds causing large potency effects are of particular interest in medicinal chemistry and drug design. Through systematic analysis, it was found that individual atom replacements were often decisive for activity cliff formation. X-ray structures of targets in complex with cliff compounds were available for a limited number of activity cliffs, aiding in rationalizing potency alterations among analogs with single- or dual-atom replacements.
CHEMICAL BIOLOGY & DRUG DESIGN
(2022)
Article
Hematology
Sarah J. Waldis, Stacey Uter, Donna Kavitsky, Cynthia Flickinger, Sunitha Vege, David F. Friedman, Connie M. Westhoff, Stella T. Chou
Summary: This study investigated alloimmunization in chronically transfused thalassemia patients of diverse races after implementing Rh and K matching, finding a rate of 0.26 antibodies per 100 units transfused. Despite lack of corresponding antigens, Rh antibodies were detected in some patients, suggesting a need for more precise matching. Extension of matching criteria to include c and e antigens could further minimize Rh alloimmunization.
Article
Biochemistry & Molecular Biology
Jose Maria Zapico, Lourdes Acosta, Miryam Pastor, Loganathan Rangasamy, Laura Marquez-Cantudo, Claire Coderch, Irene Ortin, Maria Nicolau-Sanus, Leonor Puchades-Carrasco, Antonio Pineda-Lucena, Alejandro Majali-Martinez, Pilar Ramos, Beatriz de Pascual-Teresa, Ana Ramos
Summary: This study focuses on exploring a new treatment for osteoarthritis by inhibiting MMP-13 to prevent disease progression, identifying a potential inhibitor with promising activity. The results show that compound 9a has good solubility and inhibitory activity, making it suitable for use in MG-63 human osteosarcoma cells.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2021)