Andrew T. McNutt

946 total citations · 2 hit papers
7 papers, 476 citations indexed

About

Andrew T. McNutt is a scholar working on Molecular Biology, Computational Theory and Mathematics and Organic Chemistry. According to data from OpenAlex, Andrew T. McNutt has authored 7 papers receiving a total of 476 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Molecular Biology, 5 papers in Computational Theory and Mathematics and 2 papers in Organic Chemistry. Recurrent topics in Andrew T. McNutt's work include Computational Drug Discovery Methods (5 papers), vaccines and immunoinformatics approaches (2 papers) and Click Chemistry and Applications (2 papers). Andrew T. McNutt is often cited by papers focused on Computational Drug Discovery Methods (5 papers), vaccines and immunoinformatics approaches (2 papers) and Click Chemistry and Applications (2 papers). Andrew T. McNutt collaborates with scholars based in United States, United Kingdom and India. Andrew T. McNutt's co-authors include David Ryan Koes, Rishal Aggarwal, Rocco Meli, Tomohide Masuda, Paul Francoeur, Matthew Ragoza, Jocelyn Sunseri, Yanjing Li, Geoffrey Hutchison and Tarek M. El‐Achkar and has published in prestigious journals such as Journal of Chemical Information and Modeling, Laboratory Investigation and Journal of Computer-Aided Molecular Design.

In The Last Decade

Andrew T. McNutt

7 papers receiving 465 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

GNINA 1.0: molecular docking with deep learning

2021 397 citations Andrew T. McNutt, Paul Francoeur et al. Journal of Cheminformatics profile →
GNINA 1.3: the next increment in molecular docking with deep learning

2025 19 citations Andrew T. McNutt, Yanjing Li et al. Journal of Cheminformatics profile →

Peers

Countries citing papers authored by Andrew T. McNutt

Since Specialization

Citations

This map shows the geographic impact of Andrew T. McNutt's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Andrew T. McNutt with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andrew T. McNutt more than expected).

Fields of papers citing papers by Andrew T. McNutt

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Andrew T. McNutt. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Andrew T. McNutt. The network helps show where Andrew T. McNutt may publish in the future.

Co-authorship network of co-authors of Andrew T. McNutt

This figure shows the co-authorship network connecting the top 25 collaborators of Andrew T. McNutt. A scholar is included among the top collaborators of Andrew T. McNutt based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Andrew T. McNutt. Andrew T. McNutt is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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7 of 7 papers shown

#	Work	Indexed citations
1	GNINA 1.3: the next increment in molecular docking with deep learning breakdown → 2025 ·Journal of Cheminformatics ·Andrew T. McNutt, Yanjing Li, Rocco Meli, Rishal Aggarwal, David Ryan Koes	19
2	Conformer Generation for Structure-Based Drug Design: How Many and How Good? 2023 ·Journal of Chemical Information and Modeling ·Andrew T. McNutt, (unknown), (unknown), (unknown), Geoffrey Hutchison, David Ryan Koes	22
3	Integrated Cytometry With Machine Learning Applied to High-Content Imaging of Human Kidney Tissue for In Situ Cell Classification and Neighborhood Analysis 2023 ·Laboratory Investigation ·Seth Winfree, Andrew T. McNutt, (unknown), (unknown), Daria Barwinska, Angela R. Sabo, Michael J. Ferkowicz, James C. Williams, James E. Lingeman, Connor J. Gulbronson, Katherine J. Kelly, Timothy A. Sutton, Pierre C. Dagher, Michael T. Eadon, Kenneth W. Dunn, Tarek M. El‐Achkar	5
4	Open-ComBind: harnessing unlabeled data for improved binding pose prediction 2023 ·Journal of Computer-Aided Molecular Design ·Andrew T. McNutt, David Ryan Koes	1
5	Improving ΔΔG Predictions with a Multitask Convolutional Siamese Network 2022 ·Journal of Chemical Information and Modeling ·Andrew T. McNutt, David Ryan Koes	21
6	GNINA 1.0: molecular docking with deep learning breakdown → 2021 ·Journal of Cheminformatics ·Andrew T. McNutt, Paul Francoeur, Rishal Aggarwal, Tomohide Masuda, Rocco Meli, Matthew Ragoza, Jocelyn Sunseri, David Ryan Koes	397
7	In Situ Classification of Cell Types in Human Kidney Tissue Using 3D Nuclear Staining 2020 ·Cytometry Part A ·(unknown), (unknown), (unknown), Andrew T. McNutt, (unknown), Daria Barwinska, Michael J. Ferkowicz, Michael T. Eadon, Katherine J. Kelly, Kenneth W. Dunn, Mohammad Al Hasan, Tarek M. El‐Achkar, Seth Winfree	11

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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