Synergy and Complementarity between Focused Machine Learning and Physics-Based Simulation in Affinity Prediction
They present results on the extent to which physics-based simulation (exemplified by FEP+) and focused machine learning (exemplified by QuanSA) are complementary for ligand affinity prediction. For both methods, predictions of activity for LFA-1 inhibitors from a medicinal chemistry lead optimization project were accurate within the applicable domain of each approach. A hybrid model that combined predictions by both approaches by simple averaging performed better than either method, with respect to both ranking and absolute pKi values. Two publicly available FEP+ benchmarks, covering 16 diverse biological targets, were used to test the generality of the synergy. By identifying training data specifically focused on relevant ligands, accurate QuanSA models were derived using ligand activity data known at the time of the original series publications. Results across the 16 benchmark targets demonstrated significant improvements both for ranking and for absolute pKi values using hybrid predictions that combined the FEP+ and QuanSA predicted affinity values. The results argue for a combined approach for affinity prediction that makes use of physics-driven methods as well as those driven by machine learning, each applied carefully on appropriate compounds, with hybrid prediction strategies being employed where possible.
Binding affinity prediction continues to be a challenge for computer-aided drug design, especially in the case where there is no high-resolution experimental structure of the target of interest. Even when structures of the biological target are available, affinity prediction is difficult. Simulation oriented physics-based methods, such as MM/PBSA or MM/GBSA or free energy perturbation (FEP), share a key attraction: in principle, these approaches are congruent with what is known physically. The former methods nominally predict absolute binding free energy. In terms of predictive accuracy, even in the case where experimental structures are known for all ligands under consideration, performance has been observed to be quite variable on a per-target basis, though more consistent results have been obtained in some cases, with careful application.
Overview of the QuanSA method. Beginning from ligand structures and activities (here against LFA-1), a multiple-ligand alignment is produced (with variants for each molecule), after which a smooth, nonlinear function is induced (called a “pocket field”), into which new molecules can be flexibly fit as is commonly done with docking approaches. Here, the new test molecule, compound 4, was made 7 months after the last molecule within the training set (example molecules 1–3), and it was accurately predicted. Shown in the lower row is the predicted pose of compound 4, the surface surrounded by the pocket field (left), and the interactions with the pocket field with and without the surface (middle and right).
For the FEP approach, relative free energy predictions are made. This is done by estimating the difference in the free energies of protein–ligand complexes between related ligand pairs (typically differing relatively modestly in their substituents). Advances in force fields, sampling methods, and automated design of perturbation graphs can help to guide fine-grained molecular optimization. In cases where the FEP+ method is applicable, for single perturbations of a few ligand atoms from a known reference ligand, errors in predicting changes in free energy have been reported to be as low as 0.5 pKi units (0.9 kcal/mol). More recent benchmarking on a more challenging set of perturbations yielded errors roughly 50% higher. Affinity prediction remains a challenging problem, even in cases where targets have well-characterized structures and there is little uncertainty in ligand binding modes.
Preparation and scoring procedures using a temporally segregated set of LFA-1 inhibitors from a medicinal chemistry lead optimization project: QuanSA (left) and FEP+ (right). The QuanSA approach follows a machine-learning paradigm, employing a training set and a holdout set for model selection. The FEP+ approach combines careful force field parameter estimation, molecular docking, and extensive physical simulation.
Machine-learning approaches have seen a recent resurgence in their applications within the CADD field, in part driven by advances in deep-learning methodologies. A recent review highlights a number of successful applications as well as limitations, with further context provided by a full book treatment. With respect to binding affinity prediction in the context of lead optimization, a critical factor is that the methods typically require thousands of data points in order to learn effectively, because of the need to develop encoded internal representations that meaningfully capture the important aspects required for prediction. Early-stage lead optimization may involve just dozens of assayed molecules within a newly discovered chemical series, and even mid-to-late-stage projects may be limited to hundreds or up to a few thousand data points. The recently introduced QuanSA machine-learning method (Quantitative Surface-field Analysis) differs from the deep-learning paradigm and from historically widely used methods.
Plot of experimental activities versus predicted activities from QuanSA for the full set of 67 future test molecules. Test molecules 5–8 have structures significantly different from those of the training compounds, and the plot points for these compounds are highlighted in orange. Also shown are the top pose families and interactions with the pocket field for four example test molecules with the spirocyclic pyrrolidine scaffold (9–12) whose points on the graph are highlighted in blue and are indicated with red arrows.
The central difference is that, rather than applying a generic machine-learning approach to an input molecular representation divorced from a binding event, QuanSA builds a physically interpretable model that is analogous to a protein binding site. By doing so, it addresses the problem of ligand conformation and alignment fully automatically, and it moves in the direction of causal modeling, where the requirement for data can be reduced. The method constructs a nonlinear “pocket field” that is still physical in nature, and which is directly related to the functional form of scoring functions for docking. QuanSA pocket-field models mirror key physical phenomena that are observed in protein–ligand interactions: (1) choice of ligand poses is defined by the model; (2) non-additive (or even anti-additive) effects of substituent changes on a central scaffold can be modeled effectively; (3) changes in ligand structures induce changes in predicted ligand poses; (4) the model of molecular activity is dependent on the detailed shape of ligands. Nearly all QSAR and deep-learning methods ignore some or all of these aspects of protein–ligand interactions. Additional discussion of the theoretical contrasts between the QuanSA multiple-instance learning approach and other QSAR (3D and 2D) approaches can be found in the papers introducing the method along with the antecedent QMOD and Compassapproaches, the latter of which introduced the multiple-instance machine-learning paradigm.
FEP+ reference ligand (13) and four test molecules (4 and 14–16) are shown. FEP+ employs an initial docked pose of the reference molecule in the LFA-1 binding pocket. The top pose family of the reference ligand resulting from ensemble docking using Surflex-Dock is shown to illustrate the potential conformational variation of the ligand in the protein pocket.
Four examples from comparisons of activity predictions on a 17 molecule subset of the blind test set. For QuanSA, the top pose family for each test molecule plus the interaction sticks of the top pose with the pocket field is shown. For FEP+, the initial docked poses are shown. Hybrid predicted pKi values are the simple average of the QuanSA and FEP+ values.
Comparisons of activity predictions on a 17 molecule subset of the blind test set for QuanSA, FEP+, and hybrid methods.
Here, they explore the performance of both FEP+ and the QuanSA machine-learning method in a lead optimization project application scenario and using two publicly available FEP+ benchmarks, spanning 16 diverse targets and covering affinity predictions for nearly 400 molecules. Project data for LFA-1 was used as a representative example of mid-to-late-stage lead optimization, where substantial structure–activity data exist, particularly within a chemical series of interest. T
Process of constructing a focused QuanSA model from diverse data for SHP2.
Examples of novel ligands for each of three targets from the Abel benchmark identified through temporally prospective screening of focused QuanSA models.
Accuracy of the QuanSA and FEP+ approaches, as well as a hybrid approach combining predictions from the two methods by simple averaging, will be detailed in what follows. In addition, because the QuanSA approach can be practically applied to screen large databases for new lead discovery and scaffold replacement, a screening utility was assessed using structure–activity data for diverse compounds that were disclosed after the data used for model induction.
Synergy and Complementarity between Focused Machine Learning and Physics-Based Simulation in Affinity Prediction Ann E. Cleves, Stephen R. Johnson, and Ajay N. Jain Journal of Chemical Information and Modeling 2021 61 (12), 5948-5966 DOI: 10.1021/acs.jcim.1c01382