Abstract
Effective inhibitors for the epidermal growth factor receptor (EGFR) still present a major obstacle in the development of cancer drugs. In this work, we create new EGFR inhibitors using generative artificial intelligence (AI) by fine-tuning a GPT-2 model on a dataset comprising around 500,000 molecules from the ChEMBL database. We evaluate our method against a Long Short-Term Memory (LSTM) network trained on the same dataset to create benchmarks. Before being filtered depending on Lipinski's rule of five, synthetic accessibility, and drug-likeness scores, the produced compounds were evaluated for validity, distinctiveness, and novelty. To assess their binding affinities, chosen candidates underwent molecular docking experiments against EGFR (PDB ID: 1M17). Our results show that although GPT-2 excels in producing structurally varied molecules, the LSTM model generates a larger fraction of chemically valid compounds. Many produced candidates showed good binding interactions with EGFR, therefore highlighting the possibilities of deep learning-based generative models in hastening the early phases of therapeutic development. This work presents a scalable method for creating tailored cancer treatments by showing the synergy between AI and in silico drug design.
Received: 24 February 2026 | Revised: 21 July 2026 | Accepted: 3 September 2026
Conflicts of Interest
The authors declare that they have no conflicts of interest to this work.
Data Availability Statement
The data that support this work are available upon reasonable request to the corresponding author.
Author Contribution Statement
Omar Dasser: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing– original draft, Writing– review & editing, Visualization, Supervision, Project administration. Othmane Filali benaceur: Software, Visualization. Salma Fadel: Validation, Inves tigation, Writing– review & editing. Rim Kaanane: Validation, Formal analysis, Investigation, Writing– review & editing.