TL;DR
Recent advances at the interface of AI and antibody drug discovery are highlighted, key methodological developments are discussed, and the challenges that remain in translating AI-driven strategies into clinical success are examined.
This study reviews AI-driven drug discovery methods, presents a structured AI pipeline from data collection to candidate selection, and evaluates performance using metrics such as prediction accuracy, screening efficiency, lead optimization success, and toxicity reduction.
AutoML mitigates issues by automating key stages of the ML pipeline data preprocessing, model selection, hyperparameter tuning, and evaluation thereby enhancing scalability and reducing dependence on domain expertise.
This review examines how machine learning, deep learning, natural language processing (NLP), and generative modeling are being applied across medicinal chemistry and drug development, and highlights how multimodal data fusion, predictive modeling, and human-AI collaborative frameworks are supporting more informed decisions in rational drug design.
This review emphasizes the influence of new AI platforms like AlphaFold3, molecular interactions are structurally optimized (MISATO), and ZairaChem on the discovery of oncology drugs and examines how AI reconciles chemical design with pharmacological feasibility.
This review highlights the synergy between AI and HTS, emphasizing DL techniques such as convolutional neural networks for bioactivity prediction, recurrent neural networks for de novo design, and reinforcement learning for property optimization.
Artificial intelligence (AI) is evolving from a predictive tool into a foundational computational infrastructure for mechanism-driven pharmacology, fundamentally reshaping drug discovery. This review examines how this transformation addresses persistent challenges in target validation, including data biases and the need for model interpretability, by integrating network pharmacology with advanced deep learning architectures. Specifically, graph neural networks decipher the complex topology of biological systems and transformer models facilitate the fusion of multimodal data, from genomics to real-world clinical records. Coupled with physics-informed neural networks, this integrated framework operates as a predictive computational microscope. It enables comprehensive in silico simulations that span multiple biological scales, encompassing atomic-level molecular interactions and longitudinal patient trajectories. We demonstrate that this AI-driven paradigm is essential for advancing precision medicine, as it systematically translates vast and heterogeneous datasets into testable mechanistic hypotheses. Consequently, this approach accelerates the development of safer, more effective and patient-specific therapies, by de-risking target validation and elucidating novel therapeutic mechanisms. It directly addresses some of the most pressing inefficiencies in contemporary drug discovery and development, offering a pathway towards more rational and efficient therapeutic innovation.