Biography and Research Information
OverviewAI-generated summary
Rachael Oluwakamiye Abolade's research focuses on the application of computational methods, including machine learning, molecular docking, and molecular dynamics simulations, for drug discovery and repositioning. Her work has investigated potential inhibitors for targets such as human aromatase, mitotic kinesin Eg5, and HIV-1 reverse transcriptase. Abolade also explores the use of generative artificial intelligence and transfer learning in designing novel therapeutic compounds. Her research extends to addressing complex biological questions, such as the impact of genomic and environmental factors on health, utilizing accelerated computing and machine learning approaches. She has a h-index of 2 with 16 citations across 6 publications. Key collaborators include Mujeebat Bashiru and Izzati Ibrahim.
Metrics
- h-index: 3
- Publications: 6
- Citations: 19
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
Izzati Ibrahim
Arkansas State University
2 shared publications
In database
- 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
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
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