M. A. Hakim Newton

813 total citations
47 papers, 456 citations indexed

About

M. A. Hakim Newton is a scholar working on Molecular Biology, Industrial and Manufacturing Engineering and Computer Networks and Communications. According to data from OpenAlex, M. A. Hakim Newton has authored 47 papers receiving a total of 456 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Molecular Biology, 12 papers in Industrial and Manufacturing Engineering and 11 papers in Computer Networks and Communications. Recurrent topics in M. A. Hakim Newton's work include Protein Structure and Dynamics (20 papers), Machine Learning in Bioinformatics (19 papers) and Computational Drug Discovery Methods (8 papers). M. A. Hakim Newton is often cited by papers focused on Protein Structure and Dynamics (20 papers), Machine Learning in Bioinformatics (19 papers) and Computational Drug Discovery Methods (8 papers). M. A. Hakim Newton collaborates with scholars based in Australia, United States and Bangladesh. M. A. Hakim Newton's co-authors include Abdul Sattar, Avinash Mishra, Kaile Su, Swakkhar Shatabda, Mahmood A. Rashid, John Levine, Maria Fox, Derek Long, Duc Nghia Pham and Abdollah Dehzangi and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Expert Systems with Applications.

In The Last Decade

M. A. Hakim Newton

45 papers receiving 436 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
M. A. Hakim Newton Australia	13	150	131	114	97	64	47	456
Daniel Kobler Switzerland	13	57 0.4×	108 0.8×	297 2.6×	107 1.1×	5 0.1×	21	561
Gunnar Mathiason Sweden	7	86 0.6×	19 0.1×	128 1.1×	29 0.3×	84 1.3×	22	303
Heba Askr Egypt	9	99 0.7×	13 0.1×	136 1.2×	98 1.0×	31 0.5×	16	339
Ayan Chatterjee United Kingdom	6	55 0.4×	17 0.1×	51 0.4×	100 1.0×	13 0.2×	18	289
Shuangbao Song Japan	11	82 0.5×	12 0.1×	96 0.8×	215 2.2×	12 0.2×	23	353
Juan R. González Spain	8	36 0.2×	39 0.3×	166 1.5×	243 2.5×	13 0.2×	12	324
Jiřı́ Kubalı́k Czechia	10	31 0.2×	22 0.2×	66 0.6×	160 1.6×	9 0.1×	37	263
Noureddin Sadawi United Kingdom	9	104 0.7×	55 0.4×	80 0.7×	54 0.6×	43 0.7×	13	312

Countries citing papers authored by M. A. Hakim Newton

Since Specialization

Citations

This map shows the geographic impact of M. A. Hakim Newton'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 M. A. Hakim Newton with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M. A. Hakim Newton more than expected).

Fields of papers citing papers by M. A. Hakim Newton

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by M. A. Hakim Newton. 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 M. A. Hakim Newton. The network helps show where M. A. Hakim Newton may publish in the future.

Co-authorship network of co-authors of M. A. Hakim Newton

This figure shows the co-authorship network connecting the top 25 collaborators of M. A. Hakim Newton. A scholar is included among the top collaborators of M. A. Hakim Newton 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 M. A. Hakim Newton. M. A. Hakim Newton is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Newton, M. A. Hakim, et al.. (2024). Ensembling methods for protein-ligand binding affinity prediction. Scientific Reports. 14(1). 24447–24447. 3 indexed citations

Newton, M. A. Hakim, et al.. (2023). Real-to-bin conversion for protein residue distances. Computational Biology and Chemistry. 104. 107834–107834. 1 indexed citations

Newton, M. A. Hakim, et al.. (2023). Solving travelling thief problems using coordination based methods. Journal of Heuristics. 29(4-6). 487–544. 3 indexed citations

Newton, M. A. Hakim, et al.. (2022). Constraint Guided Neighbor Generation for Protein Structure Prediction. IEEE Access. 10. 54991–55001. 3 indexed citations

Newton, M. A. Hakim, et al.. (2022). Constraint Guided Beta-Sheet Refinement for Protein Structure Prediction. Computational Biology and Chemistry. 101. 107773–107773. 1 indexed citations

Newton, M. A. Hakim, et al.. (2022). Secondary structure specific simpler prediction models for protein backbone angles. BMC Bioinformatics. 23(1). 6–6. 7 indexed citations

Newton, M. A. Hakim, et al.. (2021). Evaluating logic gate constraints in local search for structured satisfiability problems. Artificial Intelligence Review. 54(7). 5347–5411. 1 indexed citations

Newton, M. A. Hakim, et al.. (2021). Author Correction: Enhancing protein backbone angle prediction by using simpler models of deep neural networks. Scientific Reports. 11(1). 18072–18072. 1 indexed citations

Newton, M. A. Hakim, et al.. (2020). Enhancing protein backbone angle prediction by using simpler models of deep neural networks. Scientific Reports. 10(1). 19430–19430. 19 indexed citations

10.

Mishra, Avinash, et al.. (2019). Efficient Toxicity Prediction via Simple Features Using Shallow Neural Networks and Decision Trees. ACS Omega. 4(1). 1874–1888. 51 indexed citations

11.

Newton, M. A. Hakim, et al.. (2019). Scheduling blocking flowshops with setup times via constraint guided and accelerated local search. Computers & Operations Research. 109. 64–76. 17 indexed citations

12.

Newton, M. A. Hakim, et al.. (2018). Constraint guided search for aircraft sequencing. Expert Systems with Applications. 118. 440–458. 13 indexed citations

13.

Newton, M. A. Hakim, et al.. (2017). Scatter search for mixed blocking flowshop scheduling. Expert Systems with Applications. 79. 20–32. 47 indexed citations

14.

Rashid, Mahmood A., Swakkhar Shatabda, M. A. Hakim Newton, Md Tamjidul Hoque, & Abdul Sattar. (2014). A Parallel Framework for Multipoint Spiral Search in ab Initio Protein Structure Prediction. SHILAP Revista de lepidopterología. 2014. 1–17. 2 indexed citations

15.

Shatabda, Swakkhar, M. A. Hakim Newton, Mahmood A. Rashid, Duc Nghia Pham, & Abdul Sattar. (2014). How Good Are Simplified Models for Protein Structure Prediction?. PubMed. 2014. 1–9. 9 indexed citations

16.

Rashid, Mahmood A., M. A. Hakim Newton, Md Tamjidul Hoque, & Abdul Sattar. (2013). Collaborative Parallel Local Search for Simplified Protein Structure Prediction. Victoria University Research Repository (Victoria University). 966–973. 3 indexed citations

17.

Pham, Duc Nghia, et al.. (2013). Weight-enhanced diversification in stochastic local search for satisfiability. University of Southern Queensland ePrints (University of Southern Queensland). 524–530. 5 indexed citations

18.

Rashid, Mahmood A., Swakkhar Shatabda, M. A. Hakim Newton, et al.. (2012). Random-walk. Griffith Research Online (Griffith University, Queensland, Australia). 620–622. 11 indexed citations

19.

Newton, M. A. Hakim & John Levine. (2007). Wizard: Suggesting macro-actions comprehensively. 1 indexed citations

20.

Akbar, Md. Mostofa, et al.. (2007). A multiprocessor based heuristic for multi-dimensional multiple-choice knapsack problem. The Journal of Supercomputing. 43(3). 257–280. 14 indexed citations

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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