Relative free-energy calculations for scaffold hopping-type transformations with an automated RE-EDS sampling procedure

The calculation of relative free-energy differences between different compounds plays an important role in drug design to identify potent binders for a given protein target. Most rigorous methods based on molecular dynamics simulations estimate the free-energy difference between pairs of ligands. Thus, the comparison of multiple ligands requires the construction of a “state graph”, in which the compounds are connected by alchemical transformations. The computational cost can be optimized by reducing the state graph to a minimal set of transformations. However, this may require individual adaptation of the sampling strategy if a transformation process does not converge in a given simulation time. In contrast, path-free methods like replica-exchange enveloping distribution sampling (RE-EDS) allow the sampling of multiple states within a single simulation without the pre-definition of alchemical transition paths. To optimize sampling and convergence, a set of RE-EDS parameters needs to be estimated in a pre-processing step. Here, we present an automated procedure for this step that determines all required parameters, improving the robustness and ease of use of the methodology. To illustrate the performance, the relative binding free energies are calculated for a series of checkpoint kinase 1 inhibitors containing challenging transformations in ring size, opening/closing, and extension, which reflect changes observed in scaffold hopping. The simulation of such transformations with RE-EDS can be conducted with conventional force fields and, in particular, without soft bond-stretching terms.

Identification of Isosteric Replacements of Glycosyl Domain of Ligands by Data Mining

Biologically equivalent replacements of key moieties in molecule rationalizes scaffold hopping, patent busting or R-group enumeration, yet heavily depending upon the expert-defined space therefore is subjective and might be biased to the chemistries they get used to. Most importantly, these explorations are often informatively incomplete since it is often confined within try-and-error cycle, only meaning what kind of substructures are suitable for the replacement occur, but fail to disclose the driving forces to support such interchanges. The Protein Data Bank (PDB) repository involving receptor-ligand interactional information reminds poorly exploited. However, manual screening the PDB become almost impossible to excavate the bioisosteric know-how with the exponentially increase of data. Therefore, a textual content parsing workflow is developed to automatedly mine local structural replacement (LSR) of specific structure. Taking the glycosyl domain for instance, a total of 41652 replacements that overlap on nucleotide ribose were identified and categorized based on their SMILE codes. Predominately ring system, such as aliphatic aromatic ring, yet amide and sulfonamide replacement also occurred. We believe these findings may enlighten medicinal chemists to design and optimize ligand structure using bioisosteric replacement strategy.

Deep scaffold hopping with multimodal transformer neural networks

Scaffold hopping is a central task of modern medicinal chemistry for rational drug design, which aims to design molecules of novel scaffolds sharing similar target biological activities toward known hit molecules. Traditionally, scaffolding hopping depends on searching databases of available compounds that can't exploit vast chemical space. In this study, we have re-formulated this task as a supervised molecule-to-molecule translation to generate hopped molecules novel in 2D structure but similar in 3D structure, as inspired by the fact that candidate compounds bind with their targets through 3D conformations. To efficiently train the model, we curated over 50 thousand pairs of molecules with increased bioactivity, similar 3D structure, but different 2D structure from public bioactivity database, which spanned 40 kinases commonly investigated by medicinal chemists. Moreover, we have designed a multimodal molecular transformer architecture by integrating molecular 3D conformer through a spatial graph neural network and protein sequence information through Transformer. The trained DeepHop model was shown able to generate around 70% molecules having improved bioactivity together with high 3D similarity but low 2D scaffold similarity to the template molecules. This ratio was 1.9 times higher than other state-of-the-art deep learning methods and rule- and virtual screening-based methods. Furthermore, we demonstrated that the model could generalize to new target proteins through fine-tuning with a small set of active compounds. Case studies have also shown the advantages and usefulness of DeepHop in practical scaffold hopping scenarios.

On the value of using 3D-shape and electrostatic similarities in deep generative methods

Multi-parameter optimization, the heart of drug design, is still an open challenge. Thus, improved methods for automated compounds design with multiple controlled properties are desired. Here, we present a significant extension to our previously described fragment-based reinforcement learning method (DeepFMPO) for the generation of novel molecules with optimal properties. As before, the generative process outputs optimized molecules similar to the input structures, now with the improved feature of replacing parts of these molecules with fragments of similar 3D-shape and electrostatics. By performing comparisons of 3D-fragments, we can simulate 3D properties while overcoming the notoriously difficult step of accurately describing bioactive conformations. The comparison of electrostatic potential and molecular shape is performed using the new ESP-Sim python package, allowing the calculation of state-of-the-art partial charges (e.g., RESP with B3LYP/6-31G**) obtained using the quantum chemistry program Psi4. The new improved method is demonstrated with a scaffold-hopping exercise identifying CDK2 bioisosteres. All code is open-source and freely available.