TL;DR
This chapter presents the perspective of computational chemists at Sygnature Discovery on the growing need to integrate quantum mechanics (QM) with artificial intelligence (AI) to improve the efficiency and effectiveness of modern drug design.
A state-aware functional classifier (SAFC) is developed that integrates molecular dynamics derived receptor ensembles, ensemble docking and protein ligand interaction graphs that provides dynamics-aware functional activity rankings for generated molecules that were partly complementary to docking, drug-likeness and synthetic accessibility scores.
Key QM applications-torsional profiles, spectra prediction, reactivity analysis, and modeling of non-covalent interactions-highlighting their impact and limitations are reviewed, illustrating the trade-off between speed and accuracy.
This chapter reviews how QM methods can guide synthesis planning by complementing chemist expertise, literature precedent, and computer-assisted synthesis planning (CASP) tools and demonstrates how QM-driven synthesis planning can support the identification of synthetically accessible drug candidates and advance progress in making the compounds.
Recent developments in QM-based molecular docking and HTD are reviewed, focusing on cases where QM is used explicitly in docking workflows.
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.
Heterocyclic scaffolds are vital to medicinal chemistry due to their versatility, diversity, and ability to target various biological molecules. This review covers advances in designing and synthesizing bioactive heterocycles, highlighting structure-based drug design (SBDD) and ligand-based drug design (LBDD) approaches with computational modeling and Artificial Intelligence (AI) to find potent, selective molecules with good Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) profiles. Case studies show the successful development of heterocyclic drugs for cancer, microbial infections, inflammation, viral infections, and Central Nervous System (CNS) disorders. Synthetic methods have evolved from classical electrophilic/nucleophilic reactions to modern techniques like multicomponent reactions, microwave synthesis, metal catalysis, and green chemistry, making frameworks more accessible. The review discusses Quantitative Structure-Activity Relationship (QSAR) studies for molecular optimization. Challenges like synthetic complexity and resistance remain, but emerging trends like machine learning, omics, and enzyme synthesis offer new opportunities. Ultimately, combining design principles and innovative methods can speed up drug discovery and enable sustainable, personalized therapies with heterocyclic pharmacophores.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
