Comprehensive, Open-Source, and Automated Workflow for Multisite λ-Dynamics in Lead Optimization.

Multisite λ-dynamics (MSLD) is a highly efficient binding free energy calculation method that samples multiple ligands in a single round by assigning different λ values to the alchemical part of each ligand. This method holds great promise for lead optimization (LO) in drug discovery. However, the complex data preparation and simulation process limits its widespread application in diverse protein-ligand systems. To address this challenge, we developed a comprehensive, open-source, and automated workflow for MSLD calculations based on the BLaDE dynamics engine. This workflow incorporates the Ligand Internal and Cartesian coordinate reconstruction-based alignment algorithm (LIC-align) and an optimized maximum common substructure (MCS) search algorithm to accurately generate MSLD multiple topologies with ideal perturbation patterns. Furthermore, our workflow is highly modularized, allowing straightforward integration and extension of various simulation techniques, and is highly accessible to nonexperts. This workflow was validated by calculating the relative binding free energies of large-scale congeneric ligands, many of which have large perturbing groups. The agreement between the calculations and experiments was excellent, with an average unsigned error of 1.08 ± 0.47 kcal/mol. More than 57.1% of the ligands had an error of less than 1.0 kcal/mol, and the perturbations of 6 targets were fully connected via the calculations, while those of 2 targets were connected via both calculations and experimental data. The Pearson correlation coefficient reached 0.88, indicating that the MSLD workflow provides accurate predictions that can guide lead optimization in drug discovery. We also examined the impact of single-site versus multisite perturbations, ligand grouping by perturbing group size, and the position of the anchor atom on the MSLD performance. By integrating our proposed LIC-align and optimized MCS search algorithm along with the coping strategies to handle challenging molecular substructures, our workflow can handle many realistic scenarios more reasonably than all previously published methods. Moreover, we observed that our MSLD workflow achieved similar accuracy to free energy perturbation (FEP) while improving computational efficiency by over 1 order of magnitude in speedup. These findings provide valuable insights and strategies for further MSLD development, making MSLD a competitive tool for lead optimization.

A property-oriented adaptive design framework for rapid discovery of energetic molecules based on small-scale labeled datasets.

It remains an important challenge to apply machine learning in material discovery with limited-scale datasets available, in particular for the energetic materials. Motivated by the challenge, we developed a Property-oriented Adaptive Design Framework (PADF) to quickly design new energetic compounds with desired properties. The PADF consists of a search space, machine learning model, optimization algorithm and an evaluator based on quantum mechanical calculations. The effectiveness and generality of the PADF were assessed by two case studies on the heat of formation and heat of explosion as the target properties. 88 compounds were selected as the initial training dataset from the search space containing 84 083 compounds generated. SVR.lin/Trade-off coupled with E-state + SOB and KRR/KG coupled with CDS + E-state + SOB were determined to be the best combination pairs for the heat of formation and the heat of explosion, respectively. Most of the ten compounds selected from the first ten iterations exhibit better properties than the optimal sample in the initial dataset. Besides, the heat of explosion as the target property outperforms the heat of formation in designing energetic compounds with high detonation performance. In particular, a new compound selected at the 3rd iteration exhibits high potential as an explosive. Our strategy could be extended to other domains limited by small-scale datasets labeled.