Match tier
Listed
Presence
Current · Arkansas
Last published
2025
Sources
OpenAlex · ORCID
Refreshed
2026-08-15
R
Rachael Oluwakamiye Abolade
Researcher
Also affiliated: Nigerian Defence Academy (2024–2025); Lagos State Health Service Commission (2025)
Unknown Researcher
3 h-index
6 pubs
23 cited
Research Areas
Biomedical Subjects
Biography and Research Information
OverviewAI-generated summary
Rachael Oluwakamiye Abolade's research focuses on the application of machine learning and computational methods for drug discovery and design. Her work involves utilizing techniques such as molecular docking, molecular dynamics simulations, and generative artificial intelligence to identify and design novel inhibitors for various biological targets. Recent publications demonstrate her focus on developing machine learning models for drug repositioning, predicting bioactivity of potential drug candidates, and discovering new inhibitors for targets including aromatase, mitotic kinesin Eg5, HDAC3, and HIV-1 RT.
Abolade also investigates the use of advanced computing and machine learning to address complex biological questions, including the impact of genomic and environmental factors on health. Her research network includes collaborators from institutions such as the University of Arkansas at Little Rock and Arkansas State University, with whom she has co-authored multiple publications. Her scholarly output includes six publications and has garnered 20 citations, with an h-index of 3.
Metrics
- h-index: 3
- Publications: 6
- Citations: 23
Selected Publications
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Design and Discovery of New HIV‐1 RT Inhibitors Using Generative AI, Virtual Screening, and Molecular Dynamics Simulations (2025)
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De novo design and bioactivity prediction of mitotic kinesin Eg5 inhibitors using MPNN and LSTM-based transfer learning (2025)
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In silico identification of potential HDAC3 inhibitors through machine learning, molecular docking, and molecular dynamics simulations for drug repurposing (2025)
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De Novo Design and Bioactivity Prediction of Mitotic Kinesin Eg5 Inhibitors Using MPNN and LSTM-Based Transfer Learning (2025)
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Machine Learning-Based Drug Repositioning of Novel Human Aromatase Inhibitors Utilizing Molecular Docking and Molecular Dynamic Simulation (2024)
Collaboration Network
Top Collaborators
Damilola S. Bodun
Covenant University (NG)
5 shared publications
- Machine Learning-Based Drug Repositioning of Novel Human Aromatase Inhibitors Utilizing Molecular Docking and Molecular Dynamic Simulation
- De novo design and bioactivity prediction of mitotic kinesin Eg5 inhibitors using MPNN and LSTM-based transfer learning
- In silico identification of potential HDAC3 inhibitors through machine learning, molecular docking, and molecular dynamics simulations for drug repurposing
- De Novo Design and Bioactivity Prediction of Mitotic Kinesin Eg5 Inhibitors Using MPNN and LSTM-Based Transfer Learning
- Design and Discovery of New HIV‐1 RT Inhibitors Using Generative AI, Virtual Screening, and Molecular Dynamics Simulations
Mujeebat Bashiru
University of Arkansas at Little Rock
4 shared publications
In database
- De novo design and bioactivity prediction of mitotic kinesin Eg5 inhibitors using MPNN and LSTM-based transfer learning
- In silico identification of potential HDAC3 inhibitors through machine learning, molecular docking, and molecular dynamics simulations for drug repurposing
- De Novo Design and Bioactivity Prediction of Mitotic Kinesin Eg5 Inhibitors Using MPNN and LSTM-Based Transfer Learning
- Design and Discovery of New HIV‐1 RT Inhibitors Using Generative AI, Virtual Screening, and Molecular Dynamics Simulations
Adedoyin John‐Joy Owolade
Obafemi Awolowo University (NG)
4 shared publications
- De novo design and bioactivity prediction of mitotic kinesin Eg5 inhibitors using MPNN and LSTM-based transfer learning
- In silico identification of potential HDAC3 inhibitors through machine learning, molecular docking, and molecular dynamics simulations for drug repurposing
- De Novo Design and Bioactivity Prediction of Mitotic Kinesin Eg5 Inhibitors Using MPNN and LSTM-Based Transfer Learning
- Design and Discovery of New HIV‐1 RT Inhibitors Using Generative AI, Virtual Screening, and Molecular Dynamics Simulations
Wilberforce K. Ndarawit
University of Embu (KE)
3 shared publications
- De novo design and bioactivity prediction of mitotic kinesin Eg5 inhibitors using MPNN and LSTM-based transfer learning
- De Novo Design and Bioactivity Prediction of Mitotic Kinesin Eg5 Inhibitors Using MPNN and LSTM-Based Transfer Learning
- Design and Discovery of New HIV‐1 RT Inhibitors Using Generative AI, Virtual Screening, and Molecular Dynamics Simulations
Adegbenro Temitope
University of Lagos (NG)
2 shared publications
- Machine Learning-Based Drug Repositioning of Novel Human Aromatase Inhibitors Utilizing Molecular Docking and Molecular Dynamic Simulation
- De novo design and bioactivity prediction of mitotic kinesin Eg5 inhibitors using MPNN and LSTM-based transfer learning
Temitope Ibukunoluwa Adegbenro
University of Lagos (NG)
2 shared publications
- De Novo Design and Bioactivity Prediction of Mitotic Kinesin Eg5 Inhibitors Using MPNN and LSTM-Based Transfer Learning
- Design and Discovery of New HIV‐1 RT Inhibitors Using Generative AI, Virtual Screening, and Molecular Dynamics Simulations
Ezekiel A. Olugbogi
Babcock University (NG)
2 shared publications
- De novo design and bioactivity prediction of mitotic kinesin Eg5 inhibitors using MPNN and LSTM-based transfer learning
- De Novo Design and Bioactivity Prediction of Mitotic Kinesin Eg5 Inhibitors Using MPNN and LSTM-Based Transfer Learning
Katukoliya Gamage Anuththara Samadhi
American Standard (United States) (US)
2 shared publications
- De novo design and bioactivity prediction of mitotic kinesin Eg5 inhibitors using MPNN and LSTM-based transfer learning
- De Novo Design and Bioactivity Prediction of Mitotic Kinesin Eg5 Inhibitors Using MPNN and LSTM-Based Transfer Learning
Gbolahan O. Oduselu
Covenant University (NG)
2 shared publications
- De novo design and bioactivity prediction of mitotic kinesin Eg5 inhibitors using MPNN and LSTM-based transfer learning
- De Novo Design and Bioactivity Prediction of Mitotic Kinesin Eg5 Inhibitors Using MPNN and LSTM-Based Transfer Learning
Suliman Sharif
American Standard (United States) (US)
2 shared publications
- De novo design and bioactivity prediction of mitotic kinesin Eg5 inhibitors using MPNN and LSTM-based transfer learning
- De Novo Design and Bioactivity Prediction of Mitotic Kinesin Eg5 Inhibitors Using MPNN and LSTM-Based Transfer Learning
Damilola A. Omoboyowa
Adekunle Ajasin University (NG)
2 shared publications
- De novo design and bioactivity prediction of mitotic kinesin Eg5 inhibitors using MPNN and LSTM-based transfer learning
- De Novo Design and Bioactivity Prediction of Mitotic Kinesin Eg5 Inhibitors Using MPNN and LSTM-Based Transfer Learning
Olayinka O. Ajani
Covenant University (NG)
2 shared publications
- De novo design and bioactivity prediction of mitotic kinesin Eg5 inhibitors using MPNN and LSTM-based transfer learning
- De Novo Design and Bioactivity Prediction of Mitotic Kinesin Eg5 Inhibitors Using MPNN and LSTM-Based Transfer Learning
Adeniyi Ayinde Abdulwahab
Nigerian Defence Academy (NG)
1 shared publication
- Machine Learning-Based Drug Repositioning of Novel Human Aromatase Inhibitors Utilizing Molecular Docking and Molecular Dynamic Simulation
Emidun Olayinka Chidinma
Adekunle Ajasin University (NG)
1 shared publication
- Machine Learning-Based Drug Repositioning of Novel Human Aromatase Inhibitors Utilizing Molecular Docking and Molecular Dynamic Simulation
Zainab Olaide Diyaolu
Lagos State University (NG)
1 shared publication
- Machine Learning-Based Drug Repositioning of Novel Human Aromatase Inhibitors Utilizing Molecular Docking and Molecular Dynamic Simulation
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