AKT (also known as PKB) is a serine/threonine kinase that plays a pivotal regulatory role in the PI3K/AKT/mTOR signaling pathway. Dysregulation of AKT activity, especially its hyperactivation, is closely associated with the development of various human cancers and resistance to chemotherapy. Over the years, a wide array of AKT inhibitors has been discovered through experimental and computational approaches. In this regard, herein we present a comprehensive overview of AKT inhibitors identified using computer-assisted drug design methodologies (including docking-based and pharmacophore-based virtual screening, machine learning, and quantitative structure–activity relationships) and successfully validated small molecules endowed with anticancer activity. Thus, this review provides valuable insights to support scientists focused on AKT inhibition for cancer treatment and suggests untapped directions for future computer-aided drug discovery efforts.

Accurate identification of ligandable pockets is of key relevance for structure-based drug design; however, their identification remains challenging, particularly for cryptic sites that become accessible only upon conformational changes. Here, we introduce Poké, a high-performance pocket scouting tool that combines energetic mapping with a neural-network-based ligandability score, enabling rapid and reliable binding site detection in both static structures and long-timescale molecular dynamics (MD) trajectories. Poké identifies low-energy regions on protein surfaces through probe-based grid energy calculations, expands and clusters these regions into coherent pocket volumes, and computes physicochemical descriptors that are integrated into a learned ligandability estimator. The scoring model is trained on pockets identified across large data sets using docking-derived binding score distributions, allowing robust ranking of candidate sites. Benchmarking on publicly available data sets demonstrates competitive pocket detection accuracy, improved ranking performance, and substantially enhanced computational efficiency compared to widely used pocket detectors. As a representative application, we apply Poké to mixed-solvent MD simulations of TEM-1 β-lactamase, showing efficient tracking of an experimentally validated cryptic pocket and its dynamic ligandability along the trajectory. Overall, Poké enables scalable, high-throughput ligandability assessment across extensive conformational landscapes.