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Topic · Computational Theory and Mathematics

Computational Drug Discovery Methods

Computational Drug Discovery Methods is a research topic within Computational Theory and Mathematics. Science Explorer counts 78k research works in it since 1950. 9.9% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on computational methods, virtual screening, and molecular docking techniques used in drug discovery. It covers topics such as drug target identification, pharmacokinetics, chemical properties, machine learning applications, polypharmacology, and network pharmacology.

  • Molecular Docking
  • Virtual Screening
  • Drug Target Identification
  • QSAR Modeling
  • Pharmacokinetics
  • Chemical Properties
  • Machine Learning
  • Polypharmacology
  • Network Pharmacology
  • Medicinal Chemistry
Research works
78k
fractional, since 1950
In the world top 10%
7.7k
per year above
Top-10% rate
9.9%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
+42%
the tick is no change
Largest producer
China
28% of the 10 largest, 2022-now
Computational Drug Discovery Methods

Which countries lead Computational Drug Discovery Methods research?

By volume, China and India publish the most (4.2k and 3.8k works in 2022-now).

By volume, 2022-now

  1. 1 China 4.2k works
  2. 2 India 3.8k works
  3. 3 United States 3.3k works
  4. 4 Indonesia 662 works
  5. 5 United Kingdom 634 works
  6. 6 Germany 589 works
  7. 7 Saudi Arabia 518 works
  8. 8 Japan 451 works
  9. 9 Türkiye 441 works
  10. 10 Italy 436 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

China: 27.8%India: 25.1%United States: 22.1%Indonesia: 4.4%6 others listed: 20.5%28%largest
China4,165 · 27.8%India3,762 · 25.1%United States3,316 · 22.1%Indonesia662 · 4.4%6 others listed3,069 · 20.5%

Shares of the rows listed above, not of the whole node.

Which institutions lead Computational Drug Discovery Methods research?

By volume in 2022-now, Chinese Academy of Sciences publishes the most Computational Drug Discovery Methods research, followed by Centre National de la Recherche Scientifique and Russian Academy of Sciences.

By volume, 2022-now

  1. 1 Chinese Academy of SciencesChina 163 works
  2. 2 Centre National de la Recherche ScientifiqueFrance 127 works
  3. 3 Russian Academy of SciencesRussia 115 works
  4. 4 Vellore Institute of Technology UniversityIndia 94 works
  5. 5 King Saud UniversitySaudi Arabia 93 works
  6. 6 National Institutes of HealthUnited States 82 works
  7. 7 University of North Carolina at Chapel HillUnited States 82 works
  8. 8 Zhejiang UniversityChina 76 works
  9. 9 Harvard UniversityUnited States 71 works
  10. 10 SRM Institute of Science and TechnologyIndia 69 works

Who are the leading researchers in Computational Drug Discovery Methods?

The most-cited researchers publishing on Computational Drug Discovery Methods include Eric S. Lander, Yoshua Bengio and Rob Knight.

  1. 1 Eric S. Lander United States 524k citations
  2. 2 Yoshua Bengio Canada 476k citations
  3. 3 Rob Knight United States 468k citations
  4. 4 John P. Perdew United States 436k citations
  5. 5 George Davey Smith United Kingdom 435k citations
  6. 6 Georg Kresse Austria 431k citations
  7. 7 Stacey L. Gabriel United States 402k citations
  8. 8 Peer Bork Germany 396k citations
  9. 9 Robert John Tibshirani United States 396k citations
  10. 10 Guido Kroemer France 386k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

What are the most cited papers in Computational Drug Discovery Methods?

The most cited paper in Computational Drug Discovery Methods is "A short history of SHELX" (George M. Sheldrick, 2007), with 87,903 citations. Next come "AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading" (38,730) and "A new and rapid colorimetric determination of acetylcholinesterase activity" (27,264).

Most cited papers
#PaperReleasedCitations
1 A short history of SHELX George M. Sheldrick · Acta Crystallographica Section A Foundations of Crystallography Open access 21 Dec 2007 87,903
2 AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading Oleg Trott, Arthur J. Olson · Journal of Computational Chemistry Open access 4 Jun 2009 38,730
3 A new and rapid colorimetric determination of acetylcholinesterase activity George L. Ellman, K. Diane Courtney, Valentino Andres et al. · Biochemical Pharmacology Jul 1961 27,264
4 AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility Garrett M. Morris, Ruth Huey, William Lindstrom et al. · Journal of Computational Chemistry Open access 27 Apr 2009 25,864
5 SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules Antoine Daina, Olivier Alain Michielin, Vincent Zoete · Scientific Reports Open access 3 Mar 2017 18,169
All 100 most cited papers, with filters and sorting

Where is Computational Drug Discovery Methods research done?

The largest centres of Computational Drug Discovery Methods research in 2022-now are Beijing (China), Shanghai (China), Chennai (India) and New Delhi (India). Among places with at least 20 works in it, it is an unusually large share of all research in Rahway.

Largest cities, 2022-now

  1. 1 BeijingChina 562 works
  2. 2 ShanghaiChina 331 works
  3. 3 ChennaiIndia 279 works
  4. 4 New DelhiIndia 220 works
  5. 5 GuangzhouChina 195 works
  6. 6 SeoulSouth Korea 189 works
  7. 7 NanjingChina 188 works
  8. 8 RiyadhSaudi Arabia 187 works
  9. 9 HangzhouChina 180 works
  10. 10 MoscowRussia 170 works

Where it is the local speciality

  1. RahwayUS · 22.4 works12×
← less than its size predicts1×more →

Location quotient: how much more of its research is in Computational Drug Discovery Methods than the world average.

See Computational Drug Discovery Methods on the map

Where is the best place to study Computational Drug Discovery Methods?

Among universities, judged by research, Massachusetts Institute of Technology, King Saud University and Zhejiang University score highest, combining citation excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%mean 20.28%fractional works in this field (log) →share of work in the world top 10% →Massachusetts Instit…École Polytechnique …Zhejiang UniversityKing Saud University
above the meannear itbelow it

One dot per university in the first ten of the table below, at the default weights. The upper left is the interesting corner: small places doing unusually strong work.

Citation excellenceshare of its work in the world top 10%, 2022-now and all time averaged
40%
Specialisationhow much of its work is in this field, counted up to 10× the world share
10%
Sizeits output in this field, on a log scale
30%
Growthoutput growth, counted from -20% to +20%
10%
Internationalshare of its work with a co-author abroad
10%
Each slider sets a weight; the share it gets of the score is beside it.
#UniversityScoreTop 10%SpecialisationWorksGrowth
1Massachusetts Institute of TechnologyUnited States 80.629.0%3.5×46 +38.0%
2King Saud UniversitySaudi Arabia 73.512.8%5.1×93 +245.4%
3Zhejiang UniversityChina 68.319.2%1.5×76 +187.5%
4École Polytechnique Fédérale de LausanneSwitzerland 68.223.1%3.1×24 +50.0%
5Harvard UniversityUnited States 67.116.1%1.2×71 -5.1%
6Central South UniversityChina 66.918.3%1.7×62 +75.6%
7Carnegie Mellon UniversityUnited States 66.727.5%2.9×23 +72.5%
8New York UniversityUnited States 62.522.6%1.5×31 +49.1%
9University of Hassan II CasablancaMorocco 61.922.1%4.5×22 +401.2%
10King Khalid UniversitySaudi Arabia 61.512.1%5.3×37 +329.3%

Universities only. The score blends citation excellence (40%), size (30%), specialisation (10%), growth (10%), international reach (10%), 2022-now; growth compares 2018-21 with 2022-25 and counts up to +20%, since a jump from a small base is mostly noise.

Every institution in Computational Drug Discovery Methods, country by country

Is Computational Drug Discovery Methods research growing?

Output in 2022-2025 was 42% higher than in 2018-2021, peaking in 2025. The fastest-growing topics are Computational Drug Discovery Methods.

still counting19801990200020102020
grewheldshrank

The same series as a ribbon: one cell per year, darker for more. The line above answers how much; this answers when.

Its share of all research

Growing output can still be a shrinking share: all of science grows. This is Computational Drug Discovery Methods as a percentage of every work published that year, 0.094% in 2024.

still counting0.050%0.10%Computational Drug Discovery Methods0.094% in 202519801990200020102020

Fractional works of Computational Drug Discovery Methods over all fractional works that year. How the balance of science has shifted.

How much of it reaches the world top 10%

Year by year.

still counting5%10%15%Computational Drug Discovery Methods9.8% in 2025200020102020

Of each year's works in it, the share that reached the world's 10% most cited for their field and year. A field is the world for its own topics, so there is no other line to hold it against.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.

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