Article
Multidisciplinary Sciences
Fangqiang Zhu, Feliza A. Bourguet, William F. D. Bennett, Edmond Y. Lau, Kathryn T. Arrildt, Brent W. Segelke, Adam T. Zemla, Thomas A. Desautels, Daniel M. Faissol
Summary: Alchemical free energy perturbation (FEP) is a technique to calculate the free energy difference between chemical systems. This study implemented automated large-scale FEP calculations using the Amber software package for antibody design and evaluation. The FEP simulations aim to predict the effect of mutations on binding affinity and stability. Multiple strategies were incorporated to estimate the statistical uncertainties in the results. The study demonstrated the applicability of FEP in computational antibody design.
SCIENTIFIC REPORTS
(2022)
Article
Biochemistry & Molecular Biology
Esra Boz, Matthias Stein
Summary: The study utilized the CREST tool to calculate non-covalent ligand-receptor interactions and energy using the GFN2-xTB method, and blind predictions were made for the binding of 10 drug molecule ligands to the CB[8] receptor. The results demonstrate that the proposed method shows good agreement with experimental values for large molecules, showcasing the effectiveness of quantum chemistry in predicting molecular interactions.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2021)
Article
Chemistry, Analytical
Anne Stinn, Jens Furkert, Stefan H. E. Kaufmann, Pedro Moura-Alves, Michael Kolbe
Summary: The AhR is a highly conserved cellular sensor with important roles in various diseases, making it a promising target for drug development. Understanding the ligand binding properties is crucial for precise pharmacological interventions and targeted therapies.
Article
Computer Science, Artificial Intelligence
Sara de la Rosa de Saa, Maria Asuncion Lubiano, Beatriz Sinova, Maria Angeles Gil, Peter Filzmoser
Summary: This article discusses the importance of scale measures/estimates in analyzing fuzzy-valued imprecise data and introduces the concept of median distance deviation about the median (MDD) for fuzzy data sets and its robustness. The study points out that calculating MDD in fuzzy data cases is more complex and cannot be precisely computed, but the estimation method for fuzzy trapezoidal data demonstrates a certain level of robustness.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Chemistry, Medicinal
Keisuke Yanagisawa, Yoshitaka Moriwaki, Tohru Terada, Kentaro Shimizu
Summary: This study proposed a systematic method for constructing a set of cosolvents for drug discovery, named EXtended PRObes set construction by REpresentative Retrieval (EXPRORER). By extracting typical substructures from FDA-approved drugs, 138 cosolvent structures were generated, and CMD simulations were conducted for each cosolvent molecule to generate a spatial probability distribution map of cosolvent atoms (PMAP). Analysis of PMAP similarity revealed that cosolvent pairs with a PMAP similarity greater than 0.70-0.75 shared similar structural features.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2021)
Article
Chemistry, Physical
Shima Arasteh, Bin W. Zhang, Ronald M. Levy
Summary: The R-FEP-R 2.0 method is used to estimate conformational free energy changes of protein loops via an alchemical path. Unlike other sampling algorithms, R-FEP-R and R-FEP-R 2.0 do not require predetermined collective coordinates and transition pathways.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
(2021)
Review
Biochemical Research Methods
Debby D. Wang, Mengxu Zhu, Hong Yan
Summary: This paper reviews two classes of methods for accurately predicting protein-ligand binding affinities: free energy-based simulations and machine learning-based scoring functions. It follows thermodynamic cycles for the former and a feature-representation taxonomy for the latter. Additionally, recent deep learning-based predictions are also discussed, with comparisons of strengths, weaknesses, and future directions for improvements.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Multidisciplinary Sciences
Clemens Schulte, Vladimir Khayenko, Noah Frieder Nordblom, Franziska Tippel, Violetta Peck, Amit Jean Gupta, Hans Michael Maric
Summary: Protein-protein interactions are crucial for understanding physiology and pathology, with short linear motifs playing a key role. Current approaches for determining protein-peptide affinity have limitations, but the TRIC method presents a high-throughput way to analyze these interactions in solution. TRIC allows for the identification and mapping of protein interaction sites with varying affinities, providing a label-free method for determining binding affinities of unmodified peptide libraries.
Article
Chemistry, Multidisciplinary
Min Xu, Cheng Shen, Jincai Yang, Qing Wang, Niu Huang
Summary: In recent years, large-scale structure-based virtual screening has gained increasing interest for identifying novel compounds corresponding to potential drug targets. Understanding the strengths and weaknesses of docking algorithms is critical for improving their success rate in practical applications. In this study, the docking successes and failures of two representative docking programs, UCSF DOCK 3.7 and AutoDock Vina, were systematically investigated. The results showed that DOCK 3.7 performed better in early enrichment and exhibited superior computational efficiency, while Vina scoring function exhibited a bias towards compounds with higher molecular weights.
Article
Biochemical Research Methods
Brian J. Bender, Stefan Gahbauer, Andreas Luttens, Jiankun Lyu, Chase M. Webb, Reed M. Stein, Elissa A. Fink, Trent E. Balius, Jens Carlsson, John J. Irwin, Brian K. Shoichet
Summary: Structure-based docking screens of compound libraries are common in early drug and probe discovery. Best practices and control calculations are outlined to evaluate docking parameters prior to undertaking a large-scale prospective screen.
Article
Chemistry, Physical
Ziduo Yang, Weihe Zhong, Qiujie Lv, Tiejun Dong, Calvin Yu -Chian Chen
Summary: This paper proposes a geometric interaction graph neural network (GIGN) that incorporates 3D structures and physical interactions for predicting protein-ligand binding affinities. GIGN achieves state-of-the-art performance on three external test sets, and the learned representations of protein-ligand complexes are biologically meaningful.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
(2023)
Article
Chemistry, Physical
Ziduo Yang, Weihe Zhong, Qiujie Lv, Tiejun Dong, Calvin Yu-Chian Chen
Summary: This paper proposes a geometric interaction graph neural network (GIGN) that incorporates 3D structures and physical interactions for predicting protein-ligand binding affinities. GIGN achieves state-of-the-art performance on three external test sets and its predictions are shown to be biologically meaningful through visualization of learned representations of protein-ligand complexes.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
(2023)
Article
Business
Sihua Chen, Han Qiu, Xiang Wen, Bolin Wang, Wei He, Xiuyan Shao
Summary: This paper examines the determinants of people's choice of testing sites and finds that physical distance and decision information have positive influences on people's choices. The empirical study is verified by a specially designed computer simulation program. It is also found that information disclosure does not affect the overall completion rate and average time of testing, but worsens overcrowding in queues.
JOURNAL OF BUSINESS RESEARCH
(2024)
Article
Chemistry, Physical
Dmitri G. Fedorov
Summary: In this study, a many-body expansion of ionization potentials and electron affinities is developed using a combination of the fragment molecular orbital method and equation-of-motion coupled-cluster (EOM-CC). The method incorporates pair and triple corrections to account for nonlocal contributions from the molecular environment. The effect of environment on ionization potential and electron affinity is explored using carboxylic acids, alkyl cations, a protein ubiquitin, and a nano ribbon of white graphene.
JOURNAL OF PHYSICAL CHEMISTRY A
(2023)
Article
Chemistry, Multidisciplinary
Wook Lee, Jae Whee Park, Yeon Ju Go, Won Jong Kim, Young Min Rhee
Summary: VEGF(165) is a promising target for drug development, but its flexible linker has hindered structure-based studies. Computer simulation methods, such as ensemble docking, can offer a solution to this challenge and help predict ligand binding affinities for flexible proteins.
Article
Biochemistry & Molecular Biology
Geng Dong, Ulf Ryde
JOURNAL OF BIOLOGICAL INORGANIC CHEMISTRY
(2016)
Article
Biochemistry & Molecular Biology
Octav Caldararu, Martin A. Olsson, Christoph Riplinger, Frank Neese, Ulf Ryde
JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
(2017)
Article
Chemistry, Medicinal
Francesco Manzoni, Jon Uranga, Samuel Genheden, Ulf Ryde
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2017)
Article
Biochemistry & Molecular Biology
Samuel Genheden
JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
(2017)
Article
Biochemical Research Methods
Samuel Genheden
JOURNAL OF MOLECULAR GRAPHICS & MODELLING
(2017)
Article
Chemistry, Physical
Christoffer Olsson, Samuel Genheden, Victoria Garcia Sakai, Jan Swenson
JOURNAL OF PHYSICAL CHEMISTRY B
(2019)
Article
Chemistry, Multidisciplinary
Samuel Genheden, Amol Thakkar, Veronika Chadimova, Jean-Louis Reymond, Ola Engkvist, Esben Bjerrum
JOURNAL OF CHEMINFORMATICS
(2020)
Article
Chemistry, Medicinal
Samuel Genheden, Ola Engkvist, Esben Bjerrum
Summary: The algorithm efficiently computes distances between synthetic routes for clustering, showing that clustering time is short and representative routes can reduce predicted routes. The results provide intuitive clusters with similar chemistry, included in AiZynthFinder software and as a separate package.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2021)
Article
Chemistry, Medicinal
Samuel Genheden, Per-Ola Norrby, Ola Engkvist
Summary: We present AiZynthTrain Python package that trains synthesis models in a robust, reproducible, and extensible way. It includes two pipelines for creating one-step retrosynthesis and RingBreaker models, which can be easily integrated into retrosynthesis software. By training on publicly available reaction data from USPTO, these are the first reproducible end-to-end retrosynthesis models. The pipeline demonstrates improved RingBreaker performance and robustness when trained on a more diverse proprietary dataset. This framework is expected to be expanded with other synthesis models in the future.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2023)
Editorial Material
Chemistry, Organic
Michael P. Maloney, Connor W. Coley, Samuel Genheden, Nessa Carson, Paul Helquist, Per-Ola Norrby, Olaf Wiest
JOURNAL OF ORGANIC CHEMISTRY
(2023)
Editorial Material
Chemistry, Organic
Michael P. Maloney, Connor W. Coley, Samuel Genheden, Nessa Carson, Paul Helquist, Per-Ola Norrby, Olaf Wiest
Article
Computer Science, Artificial Intelligence
Samuel Genheden, Ola Engkvist, Esben Bjerrum
Summary: This article expands on recent research on clustering synthetic routes and trains a deep learning model to predict distances between different routes. The machine learning approach used in this study is considerably faster than the traditional tree edit distance method and allows for clustering a greater number of routes with similar results. The developed model is also open-source.
MACHINE LEARNING-SCIENCE AND TECHNOLOGY
(2022)
Article
Cell Biology
Lina Lindahl, Samuel Genheden, Fabio Faria-Oliveira, Stefan Allard, Leif A. Eriksson, Lisbeth Olsson, Maurizio Bettiga
Meeting Abstract
Biophysics
Samuel Genheden
BIOPHYSICAL JOURNAL
(2017)
Article
Biochemistry & Molecular Biology
Samuel Genheden, Jonathan W. Essex, Anthony G. Lee
BIOCHIMICA ET BIOPHYSICA ACTA-BIOMEMBRANES
(2017)