Neural networks in chemistry and drug design
- Authors
- Jure ZupanJohann Gasteiger
- Journal
- Medical Entomology and Zoology
In The Last Decade
doi.org/w28030516 →Countries where authors are citing Neural networks in chemistry and drug design
This map shows the geographic impact of Neural networks in chemistry and drug design. 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 Neural networks in chemistry and drug design with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Neural networks in chemistry and drug design more than expected).
Fields of papers citing Neural networks in chemistry and drug design
This network shows the impact of Neural networks in chemistry and drug design. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Neural networks in chemistry and drug design.
About Neural networks in chemistry and drug design
This paper, published in 1999, received 585 indexed citations . Written by Jure Zupan and Johann Gasteiger covering the research area of Computational Theory and Mathematics. It is primarily cited by scholars working on Computational Theory and Mathematics (279 citations), Molecular Biology (173 citations) and Analytical Chemistry (170 citations). Published in Medical Entomology and Zoology.
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.
This paper is also available at doi.org/w28030516.