Structural optimization of lead compounds is a crucial step in drug discovery. One optimization strategy is to modify the molecular structure of a scaffold to improve both its biological activities and absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. One of the deep molecular generative model approaches preserves the scaffold while generating drug-like molecules, thereby accelerating the molecular optimization process. Deep molecular diffusion generative models simulate a gradual process that creates novel, chemically feasible molecules from noise. However, the existing models lack direct interatomic constraint features and struggle with capturing long-range dependencies in macromolecules, leading to challenges in modifying the scaffold-based molecular structures, and creates limitations in the stability and diversity of the generated molecules. To address these challenges, we propose a deep molecular diffusion generative model, the three-dimensional (3D) equivariant diffusion-driven molecular generation (3D-EDiffMG) model. The dual strong and weak atomic interaction force-based long-range dependency capturing equivariant encoder (dual-SWLEE) is introduced to encode both the bonding and non-bonding information based on strong and weak atomic interactions. Additionally, a gate multilayer perceptron (gMLP) block with tiny attention is incorporated to explicitly model complex long-sequence feature interactions and long-range dependencies. The experimental results show that 3D-EDiffMG effectively generates unique, novel, stable, and diverse drug-like molecules, highlighting its potential for lead optimization and accelerating drug discovery.

Accurate receptor/ligand binding free energy calculations can greatly accelerate drug discovery by identifying highly potent ligands. By simulating the change from one compound structure to another, the relative binding free energy (RBFE) change can be calculated based on the theoretically rigorous free energy perturbation (FEP) method. However, existing FEP-RBFE approaches may face convergence challenges due to difficulties in simulating non-physical intermediate states, which can lead to increased computational costs to obtain the converged results. To fundamentally overcome these issues and accelerate drug discovery, a new combined-structure RBFE (CS-FEP) calculation strategy was proposed, which solved the existing issues by constructing a new alchemical pathway, smoothed the alchemical transformation, increased the phase-space overlap between adjacent states, and thus significantly increased the convergence and accelerated the relative binding free energy calculations. This method was extensively tested in a practical drug discovery effort by targeting phosphodiesterase-1 (PDE1). Starting from a PDE1 inhibitor (compound 9, IC₅₀ = 16.8 μmol/L), the CS-FEP guided hit-to-lead optimizations resulted in a promising lead (11b and its mesylate salt formulation 11b-Mesylate, IC₅₀ = 7.0 nmol/L), with ∼2400-fold improved inhibitory activity. Further experimental studies revealed that the lead showed reasonable metabolic stability and significant anti-fibrotic effects in vivo.

Scaffold hopping refers to computer-aided screening for active compounds with different structures against the same receptor to enrich privileged scaffolds, which is a topic of high interest in organic and medicinal chemistry. However, most approaches cannot efficiently predict the potency level of candidates after scaffold hopping. Herein, we identified potent PDE5 inhibitors with a novel scaffold via a free energy perturbation (FEP)-guided scaffold-hopping strategy, and FEP shows great advantages to precisely predict the theoretical binding potencies ΔG _FEP between ligands and their target, which were more consistent with the experimental binding potencies ΔG _EXP (the mean absolute deviations | Δ G FEP - Δ G EXP |  < 2 kcal/mol) than those ΔG _MM-PBSA or ΔG _MM-GBSA predicted by the MM-PBSA or MM-GBSA method. Lead L12 had an IC₅₀ of 8.7 nmol/L and exhibited a different binding pattern in its crystal structure with PDE5 from the famous starting drug tadalafil. Our work provides the first report via the FEP-guided scaffold hopping strategy for potent inhibitor discovery with a novel scaffold, implying that it will have a variety of future applications in rational molecular design and drug discovery.