Fast, systematic and robust relative binding free energies for simple and complex transformations : dual-LAO

TL;DR

RBFE calculations for large and complex transformations are often prohibitive; this work introduces dual-LAO, a fast RBFE strategy that merges dynamic λ sampling via $\Lambda$-ABF-OPES, dual-topology, and dual-DBC restraints implemented in AMOEBA-enabled Tinker-HP. Across a diverse set of perturbations, dual-LAO achieves an edgewise RMSE of $0.51$ kcal/mol and pairwise RMSE of $0.56$ kcal/mol with $R^{2}=0.85$, delivering a $15$–$30$-fold acceleration and converging within $4$ ns per edge (complex) and $2$ ns per edge (solvent). The methodology is further validated by a successful multi-topology extension enabling network-level RBFE sampling, demonstrating robust performance across fragment-based changes, buried-water displacement, fragment-like perturbations, and scaffold-hopping. Together, these results present a practical, high-accuracy RBFE workflow that can be integrated into drug-discovery pipelines for rapid hit-to-lead and lead optimization, with potential for coupling to machine-learning foundations.

Abstract

Relative Binding Free Energy (RBFE) calculations are a cornerstone of rational hit-to-lead and lead optimization in modern drug discovery. However, the high computational cost and limited reliability in tackling large or complex molecular transformations often prevent their routine, high-throughput use. Here we introduce dual-LAO, a novel, highly efficient method for calculating RBFE. Building on the Lambda-ABF-OPES framework, this method combines a dual-topology setup and suitable restraints to dramatically accelerate free energy convergence. We demonstrate that dual-LAO, in combination with the AMOEBA polarizable force field, achieves an unprecedented acceleration factor of 15 to 30 times compared to current state-of-the-art methods on standard drug targets. Crucially, the approach maintains high accuracy and successfully tackles previously prohibitive molecular changes, including scaffold-hopping, buried water displacement, charge changes, ring-opening, and binding pose perturbations. This significant leap in efficiency allows for the widespread, routine integration of predictive molecular simulations into the rapid optimization cycles of drug discovery, enabling chemists to confidently model historically challenging systems in timescales compatible with real-world project deadlines.

Figures (4)

• Figure 1: Scaling of the intermolecular interactions and DBC restraint as a function of $\lambda$. (a) Scaling of the intermolecular interactions for ligand $L_1$ ($\lambda_{\text{ELE}}^{L_1}$, $\lambda_{\text{VDW}}^{L_1}$) and ligand $L_2$ ($\lambda_{\text{ELE}}^{L_2}$, $\lambda_{\text{VDW}}^{L_2}$) as a function of $\lambda$. (b) Dual-DBC targets profiles ($\mathbf{d_{\mathrm{target1}}}$ and $\mathbf{d_{\mathrm{target2}}}$).
• Figure 2: RBFE Alchemical Transformations and Systems. (a) PWWP1-ligand complex: ($\text{a}_{1}$) shows a side chain transformation where the change involves only the peripheral substituent, keeping the core binding pose conserved. ($\text{a}_{2}$) illustrates a binding pose transformation, where Ligand A (green) adopts a different pose compared to Ligand B (red), leading to new protein-ligand interactions. (b) BRD4-ligand complex: ($\text{b}_{1}$) and ($\text{b}_{2}$) display Buried Water Displacement. The comparison shows the displacement of ordered buried water molecules (red and white spheres) as the ligand size increases and extends its binding pose. (c) P38-ligand complex: ($\text{c}_{1}$) and ($\text{c}_{2}$) represent Fragment-Based Perturbations, where the transformation causes a change in the ligand's interaction with the protein. (d) CHK1-ligand complex: ($\text{d}_{1}$) and ($\text{d}_{2}$) show Scaffold-Hopping Transformations, where the $\text{R}_{1}$ and $\text{R}_{2}$ side chains change from an open form to a closed ring structure.
• Figure 3: Experimental vs. Calculated Absolute Binding Free Energies ($\Delta G$) derived from $\text{RBFE}$. (a) PWWP1, (b) BRD4, (c) P38, and (d) CHK1 complexes. The calculated $\Delta G$ results and associated errors represent the mean $\pm$ standard error derived from the Weighted Least-Squares ($\text{WLS}$) fitting of the $\text{RBFE}$ network (see SI for more details). The dark and light shaded regions indicate $\pm$1 kcal/mol and $\pm$2 kcal/mol deviations from the experimental values, respectively. The color bar displays the absolute difference between the experimental and computed values, $|\Delta G_{\text{EXP}} - \Delta G_{\text{CAL}}|$. For each system, key performance statistics including Pearson's $r$, RMSE, and MAE are reported.
• Figure 4: Experimental vs. Calculated Absolute Binding Free Energies ($\Delta G$) derived from the $\text{RBFE}$ network fit for all studied systems. The calculated $\Delta G$ results and associated errors represent the mean $\pm$ standard error derived from the Weighted Least-Squares ($\text{WLS}$) fitting of the $\text{RBFE}$ network. The dark and light shaded regions indicate $\pm$1 kcal/mol and $\pm$2 kcal/mol deviations from the experimental values, respectively. The color bar displays the absolute difference between the experimental and computed values, $|\Delta G_{\text{EXP}} - \Delta G_{\text{CAL}}|$.