PharmacoNet: Accelerating Large-Scale Virtual Screening by Deep Pharmacophore Modeling

As the size of accessible compound libraries expands to over 10 billion, the need for more efficient structure-based virtual screening methods is emerging. Different pre-screening methods have been developed to rapidly screen the library, but the structure-based methods applicable to general proteins are still lacking: the challenge is to predict the binding pose between proteins and ligands and perform scoring in an extremely short time. We introduce PharmacoNet, a deep learning framework that identifies the optimal 3D pharmacophore arrangement which a ligand should have for stable binding from the binding site. By coarse-grained graph matching between ligands and the generated pharmacophore arrangement, we solve the expensive binding pose sampling and scoring procedures of existing methods in a single step. PharmacoNet is significantly faster than state-of-the-art structure-based approaches, yet reasonably accurate with a simple scoring function. Furthermore, we show the promising result that PharmacoNet effectively retains hit candidates even under the high pre-screening filtration rates. Overall, our study uncovers the hitherto untapped potential of a pharmacophore modeling approach in deep learning-based drug discovery.

C3Net: interatomic potential neural network for prediction of physicochemical properties in heterogenous systems

Understanding the interactions of a solute with its environment is of fundamental importance in chemistry and biology. In this work, we propose a deep neural network architecture for atom type embeddings in its molecular context and interatomic potential that follows fundamental physical laws. The architecture is applied to predict physicochemical properties in heterogeneous systems including solvation in diverse solvents, 1-octanol-water partitioning, and PAMPA with a single set of network weights. We show that our architecture is generalized well to the physicochemical properties and outperforms state-of-the-art approaches based on quantum mechanics and neural networks in the task of solvation free energy prediction. The interatomic potentials at each atom in a solute obtained from the model allow quantitative analysis of the physicochemical properties at atomic resolution consistent with chemical and physical reasoning. The software is available at https://github.com/SehanLee/C3Net.

PIGNet2: A Versatile Deep Learning-based Protein-Ligand Interaction Prediction Model for Binding Affinity Scoring and Virtual Screening

Prediction of protein-ligand interactions (PLI) plays a crucial role in drug discovery as it guides the identification and optimization of molecules that effectively bind to target proteins. Despite remarkable advances in deep learning-based PLI prediction, the development of a versatile model capable of accurately scoring binding affinity and conducting efficient virtual screening remains a challenge. The main obstacle in achieving this lies in the scarcity of experimental structure-affinity data, which limits the generalization ability of existing models. Here, we propose a viable solution to address this challenge by introducing a novel data augmentation strategy combined with a physics-informed graph neural network. The model showed significant improvements in both scoring and screening, outperforming task-specific deep learning models in various tests including derivative benchmarks, and notably achieving results comparable to the state-of-the-art performance based on distance likelihood learning. This demonstrates the potential of this approach to drug discovery.

A 2D Graph-Based Generative Approach For Exploring Transition States Using Diffusion Model

The exploration of transition state (TS) geometries is crucial for elucidating chemical reaction mechanisms and modeling their kinetics. In recent years, machine learning (ML) models have shown remarkable performance in TS geometry prediction. However, they require 3D geometries of reactants and products that can be challenging to determine. To tackle this, we introduce TSDiff, a novel ML model based on the stochastic diffusion method, which generates the 3D geometry of the TS from a 2D graph composed of molecular connectivity. Despite of this simple input, TSDiff generated TS geometries with high accuracy, outperforming existing ML models that utilize geometric information. Moreover, the generative model approach enabled the sampling of various valid TS conformations, even though only a single conformation for each reaction was used in training. Consequently, TSDiff also found more favorable reaction pathways with lower barrier heights than those in the reference database. We anticipate that this approach will be useful for exploring complex reactions that require the consideration of multiple TS conformations.

Predicting quantum chemical property with easy-to-obtain geometry via positional denoising

As quantum chemical properties have a significant dependence on their geometries, graph neural networks (GNNs) using 3D geometric information have achieved high prediction accuracy in many tasks. However, they often require 3D geometries obtained from high-level quantum mechanical calculations, which are practically infeasible, limiting their applicability in real-world problems. To tackle this, we propose a method to accurately predict the properties with relatively easy-to-obtain geometries (e.g., optimized geometries from the molecular force field). In this method, the input geometry, regarded as the corrupted geometry of the correct one, gradually approaches the correct one as it passes through the stacked denoising layers. We investigated the performance of the proposed method using 3D message-passing architectures for two prediction tasks: molecular properties and chemical reaction property. The reduction of positional errors through the denoising process contributed to performance improvement by increasing the mutual information between the correct and corrupted geometries. Moreover, our analysis of the correlation between denoising power and predictive accuracy demonstrates the effectiveness of the denoising process.

Fragment-based molecular generative model with high generalization ability and synthetic accessibility

Deep generative models are attracting great attention for molecular design with desired properties. Most existing models generate molecules by sequentially adding atoms. This often renders generated molecules with less correlation with target properties and low synthetic accessibility. Molecular fragments such as functional groups are more closely related to molecular properties and synthetic accessibility than atoms. Here, we propose a fragment-based molecular generative model which designs new molecules with target properties by sequentially adding molecular fragments to any given starting molecule. A key feature of our model is a high generalization ability in terms of property control and fragment types. The former becomes possible by learning the contribution of individual fragments to the target properties in an auto-regressive manner. For the latter, we used a deep neural network that predicts the bonding probability of two molecules from the embedding vectors of the two molecules as input. The high synthetic accessibility of the generated molecules is implicitly considered while preparing the fragment library with the BRICS decomposition method. We show that the model can generate molecules with the simultaneous control of multiple target properties at a high success rate. It also works equally well with unseen fragments even in the property range where the training data is rare, verifying the high generalization ability. As a practical application, we demonstrated that the model can generate potential inhibitors with high binding affinities against the 3CL protease of SARS-COV-2 in terms of docking score.

PIGNet: A physics-informed deep learning model toward generalized drug-target interaction predictions

Recently, deep neural network (DNN)-based drug-target interaction (DTI) models are highlighted for their high accuracy with affordable computational costs. Yet, the models' insufficient generalization remains a challenging problem in the practice of in-silico drug discovery. We propose two key strategies to enhance generalization in the DTI model. The first one is to integrate physical models into DNN models. Our model, PIGNet, predicts the atom-atom pairwise interactions via physics-informed equations parameterized with neural networks and provides the total binding affinity of a protein-ligand complex as their sum. We further improved the model generalization by augmenting a wider range of binding poses and ligands to training data. PIGNet achieved a significant improvement in docking success rate, screening enhancement factor, and screening success rate by up to 2.01, 10.78, 14.0 times, respectively, compared to the previous DNN models. The physics-informed model also enables the interpretation of predicted binding affinities by visualizing the energy contribution of ligand substructures, providing insights for ligand optimization. Finally, we devised the uncertainty estimator of our model's prediction to qualify the outcomes and reduce the false positive rates.

Molecular Generative Model Based On Adversarially Regularized Autoencoder

Deep generative models are attracting great attention as a new promising approach for molecular design. All models reported so far are based on either variational autoencoder (VAE) or generative adversarial network (GAN). Here we propose a new type model based on an adversarially regularized autoencoder (ARAE). It basically uses latent variables like VAE, but the distribution of the latent variables is obtained by adversarial training like in GAN. The latter is intended to avoid both inappropriate approximation of posterior distribution in VAE and difficulty in handling discrete variables in GAN. Our benchmark study showed that ARAE indeed outperformed conventional models in terms of validity, uniqueness, and novelty per generated molecule. We also demonstrated successful conditional generation of drug-like molecules with ARAE for both cases of single and multiple properties control. As a potential real-world application, we could generate EGFR inhibitors sharing the scaffolds of known active molecules while satisfying drug-like conditions simultaneously.

Scaffold-based molecular design using graph generative model

Searching new molecules in areas like drug discovery often starts from the core structures of candidate molecules to optimize the properties of interest. The way as such has called for a strategy of designing molecules retaining a particular scaffold as a substructure. On this account, our present work proposes a scaffold-based molecular generative model. The model generates molecular graphs by extending the graph of a scaffold through sequential additions of vertices and edges. In contrast to previous related models, our model guarantees the generated molecules to retain the given scaffold with certainty. Our evaluation of the model using unseen scaffolds showed the validity, uniqueness, and novelty of generated molecules as high as the case using seen scaffolds. This confirms that the model can generalize the learned chemical rules of adding atoms and bonds rather than simply memorizing the mapping from scaffolds to molecules during learning. Furthermore, despite the restraint of fixing core structures, our model could simultaneously control multiple molecular properties when generating new molecules.

Predicting drug-target interaction using 3D structure-embedded graph representations from graph neural networks

Accurate prediction of drug-target interaction (DTI) is essential for in silico drug design. For the purpose, we propose a novel approach for predicting DTI using a GNN that directly incorporates the 3D structure of a protein-ligand complex. We also apply a distance-aware graph attention algorithm with gate augmentation to increase the performance of our model. As a result, our model shows better performance than docking and other deep learning methods for both virtual screening and pose prediction. In addition, our model can reproduce the natural population distribution of active molecules and inactive molecules.