Affinity Prediction Research Articles

Graph neural pre-training based drug-target affinity prediction.

Computational drug-target affinity prediction has the potential to accelerate drug discovery. Currently, pre-training models have achieved significant success in various fields due to their ability to train the model using vast amounts of unlabeled data. However, given the scarcity of drug-target interaction data, pre-training models can only be trained separately on drug and target data, resulting in features that are insufficient for drug-target affinity prediction. To address this issue, in this paper, we design a graph neural pre-training-based drug-target affinity prediction method (GNPDTA). This approach comprises three stages. In the first stage, two pre-training models are utilized to extract low-level features from drug atom graphs and target residue graphs, leveraging a large number of unlabeled training samples. In the second stage, two 2D convolutional neural networks are employed to combine the extracted drug atom features and target residue features into high-level representations of drugs and targets. Finally, in the third stage, a predictor is used to predict the drug-target affinity. This approach fully utilizes both unlabeled and labeled training samples, enhancing the effectiveness of pre-training models for drug-target affinity prediction. In our experiments, GNPDTA outperforms other deep learning methods, validating the efficacy of our approach.

MMFA-DTA: Multimodal Feature Attention Fusion Network for Drug-Target Affinity Prediction for Drug Repurposing Against SARS-CoV-2.

The continuous emergence of novel infectious diseases poses a significant threat to global public health security, necessitating the development of small-molecule inhibitors that directly target pathogens. The RNA-dependent RNA polymerase (RdRp) and main protease (Mpro) of SARS-CoV-2 have been validated as potential key antiviral drug targets for the treatment of COVID-19. However, the conventional new drug R&D cycle takes 10-15 years, failing to meet the urgent needs during epidemics. Here, we propose a general multimodal deep learning framework for drug repurposing, MMFA-DTA, to enable rapid virtual screening of known drugs and significantly improve discovery efficiency. By extracting graph topological and sequence features from both small molecules and proteins, we design attention mechanisms to achieve dynamic fusion across modalities. Results demonstrate the superior performance of MMFA-DTA in drug-target affinity prediction over several state-of-the-art baseline methods on Davis and KIBA data sets, validating the benefits of heterogeneous information integration for representation learning and interaction modeling. Further fine-tuning on COVID-19-relevant bioactivity data enhances model predictions for critical SARS-CoV-2 enzymes. Case studies screening the FDA-approved drug library successfully identify etacrynic acid as the potential lead compound against both RdRp and Mpro. Molecular dynamics simulations further confirm the stability and binding affinity of etacrynic acid to these targets. This study proves the great potential and advantages of deep learning and drug repurposing strategies in supporting antiviral drug discovery. The proposed general and rapid response computational framework holds significance for preparedness against future public health events.

Enhancing Drug-Target Binding Affinity Prediction through Deep Learning and Protein Secondary Structure Integration

Background: Conventional approaches to drug discovery are often characterized by lengthy and costly processes. To expedite the discovery of new drugs, the integration of artificial intelligence (AI) in predicting drug-target binding affinity (DTA) has emerged as a crucial approach. Despite the proliferation of deep learning methods for DTA prediction, many of these methods primarily concentrate on the amino acid sequence of proteins. Yet, the interactions between drug compounds and targets occur within distinct segments within the protein structures, whereas the primary sequence primarily captures global protein features. Consequently, it falls short of fully elucidating the intricate relationship between drugs and their respective targets. Objective: This study aims to employ advanced deep-learning techniques to forecast DTA while incorporating information about the secondary structure of proteins. Methods: In our research, both the primary sequence of protein and the secondary structure of protein were leveraged for protein representation. While the primary sequence played the role of the overarching feature, the secondary structure was employed as the localized feature. Convolutional neural networks and graph neural networks were utilized to independently model the intricate features of target proteins and drug compounds. This approach enhanced our ability to capture drugtarget interactions more effectively Results: We have introduced a novel method for predicting DTA. In comparison to DeepDTA, our approach demonstrates significant enhancements, achieving a 3.9% increase in the Concordance Index (CI) and a remarkable 34% reduction in Mean Squared Error (MSE) when evaluated on the KIBA dataset. Conclusion: In conclusion, our results unequivocally demonstrate that augmenting DTA prediction with the inclusion of the protein's secondary structure as a localized feature yields significantly improved accuracy compared to relying solely on the primary structure.

Enhancing protein-ligand binding affinity prediction through sequential fusion of graph and convolutional neural networks.

Predicting protein-ligand binding affinity is a crucial and challenging task in structure-based drug discovery. With the accumulation of complex structures and binding affinity data, various machine-learning scoring functions, particularly those based on deep learning, have been developed for this task, exhibiting superiority over their traditional counterparts. A fusion model sequentially connecting a graph neural network (GNN) and a convolutional neural network (CNN) to predict protein-ligand binding affinity is proposed in this work. In this model, the intermediate outputs of the GNN layers, as supplementary descriptors of atomic chemical environments at different levels, are concatenated with the input features of CNN. The model demonstrates a noticeable improvement in performance on CASF-2016 benchmark compared to its constituent CNN models. The generalization ability of the model is evaluated by setting a series of thresholds for ligand extended-connectivity fingerprint similarity or protein sequence similarity between the training and test sets. Masking experiment reveals that model can capture key interaction regions. Furthermore, the fusion model is applied to a virtual screening task for a novel target, PI5P4Kα. The fusion strategy significantly improves the ability of the constituent CNN model to identify active compounds. This work offers a novel approach to enhancing the accuracy of deep learning models in predicting binding affinity through fusion strategies.

Drug-Target Binding Affinity Prediction in a Continuous Latent Space Using Variational Autoencoders.

Accurate prediction of Drug-Target binding Affinity (DTA) is a daunting yet pivotal task in the sphere of drug discovery. Over the years, a plethora of deep learning-based DTA models have emerged, rendering promising results in predicting the binding affinities between drugs and their target proteins. However, in contrast to the conventional approach of modeling binding affinity in vector spaces, we propose a more nuanced modeling process in a continuous space to account for the diversity of input samples. Initially, the drug is encoded using the Simplified Molecular Input Line Entry System (SMILES), while the target sequences are characterized via a pretrained language model. Subsequently, highly correlative information is extracted utilizing residual gated convolutional neural networks. In a departure from existing deep learning-based models, our model learns the hidden representations of the drugs and targets jointly. Instead of employing two vectors, our hidden representations consist of two Gaussian distributions. To validate the effectiveness of our proposal, we conducted evaluations on commonly utilized benchmark datasets. The experimental outcomes corroborated that our method surpasses the state-of-the-art vectorial representation methods in terms of performance. This approach, therefore, offers potential enhancements in the precision of DTA predictions, potentially contributing to more efficient drug discovery processes.

Bis(amino acidato)copper(II) compounds in blood plasma: a review of computed structural properties and amino acid affinities for Cu2+ informing further pharmacological research.

Neutral bis(amino acidato)copper(II) [Cu(aa)2] coordination compounds are the physiological species of copper(II) amino acid compounds in blood plasma taking the form of bis(l-histidinato)copper(II) and mixed ternary copper(II)-l-histidine complexes, preferably with l-glutamine, l-threonine, l-asparagine, and l-cysteine. These amino acids have three functional groups that can bind metal ions: the common α-amino and carboxylate groups and a side-chain polar group. In Cu(aa)2, two coordinating groups per amino acid bind to copper(II) in-plane, while the third group can bind apically, which yields many possibilities for axial and planar bonds, that is, for bidentate and tridentate binding. So far, the experimental studies of physiological Cu(aa)2 compounds in solutions have not specified their complete geometries. This paper provides a brief review of my group's research on structural properties of physiological Cu(aa)2 calculated using the density functional theory (DFT) to locate low-energy conformers that can coexist in aqueous solutions. These DFT investigations have revealed high conformational flexibility of ternary Cu(aa)2 compounds for tridentate or bidentate chelation, which may explain copper(II) exchange reactions in the plasma and inform the development of small multifunctional copper(II)-binding drugs with several possible copper(II)-binding groups. Furthermore, our prediction of metal ion affinities for Cu2+ binding with amino-acid ligands in low-energy conformers with different coordination modes of five physiological Cu(aa)2 in aqueous solution supports the findings of their abundance in human plasma obtained with chemical speciation modelling.

MolMVC: Enhancing molecular representations for drug-related tasks through multi-view contrastive learning.

Effective molecular representation is critical in drug development. The complex nature of molecules demands comprehensive multi-view representations, considering 1D, 2D, and 3D aspects, to capture diverse perspectives. Obtaining representations that encompass these varied structures is crucial for a holistic understanding of molecules in drug-related contexts. In this study, we introduce an innovative multi-view contrastive learning framework for molecular representation, denoted as MolMVC. Initially, we use a Transformer encoder to capture 1D sequence information and a Graph Transformer to encode the intricate 2D and 3D structural details of molecules. Our approach incorporates a novel attention-guided augmentation scheme, leveraging prior knowledge to create positive samples tailored to different molecular data views. To align multi-view molecular positive samples effectively in latent space, we introduce an adaptive multi-view contrastive loss (AMCLoss). In particular, we calculate AMCLoss at various levels within the model to effectively capture the hierarchical nature of the molecular information. Eventually, we pre-train the encoders via minimizing AMCLoss to obtain the molecular representation, which can be used for various down-stream tasks. In our experiments, we evaluate the performance of our MolMVC on multiple tasks, including molecular property prediction (MPP), drug-target binding affinity (DTA) prediction and cancer drug response (CDR) prediction. The results demonstrate that the molecular representation learned by our MolMVC can enhance the predictive accuracy on these tasks and also reduce the computational costs. Furthermore, we showcase MolMVC's efficacy in drug repositioning across a spectrum of drug-related applications. The code and pre-trained model are publicly available at https://github.com/Hhhzj-7/MolMVC.

Lycium barbarum Ameliorates Oral Mucositis via HIF and TNF Pathways: A Network Pharmacology Approach.

Oral mucositis is the most common and troublesome complication for cancer patients receiving radiotherapy or chemotherapy. Recent research has shown that Lycium barbarum, an important economic crop widely grown in China, has epithelial protective effects in several other organs. However, it is unknown whether or not Lycium barbarum can exert a beneficial effect on oral mucositis. Network pharmacology has been suggested to be applied in "multi-component-multi-target" functional food studies. The purpose of this study is to evaluate the effect of Lycium barbarum on oral mucositis through network pharmacology, molecular docking and experimental validation. To explore the biological effects and molecular mechanisms of Lycium barbarum in the treatment of oral mucositis through network pharmacology and molecular docking combined with experimental validation. Based on network pharmacology methods, we collected the active components and related targets of Lycium barbarum from public databases, as well as the targets related to oral mucositis. We mapped protein- protein interaction (PPI) networks, performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment, and constructed a 'components-disease-targets' network and 'components-pathways-targets' network using Cytoscape to further analyse the intrinsic molecular mechanisms of Lycium barbarum against oral mucositis. The affinity and stability predictions were performed using molecular docking strategies, and experiments were conducted to demonstrate the biological effects and possible mechanisms of Lycium barbarum against oral mucositis. A network was established between 49 components and 61 OM targets. The main active compounds were quercetin, beta-carotene, palmatine, and cyanin. The predicted core targets were IL-6, RELA, TP53, TNF, IL10, CTNNB1, AKT1, CDKN1A, HIF1A and MYC. The enrichment analysis predicted that the therapeutic effect was mainly through the regulation of inflammation, apoptosis, and hypoxia response with the involvement of TNF and HIF pathways. Molecular docking results showed that key components bind well to the core targets. In both chemically and radiation-induced OM models, Lycium barbarum significantly promoted healing and reduced inflammation. The experimental verification showed Lycium barbarum targeted the key genes (IL-6, RELA, TP53, TNF, IL10, CTNNB1, AKT1, CDKN1A, HIF1A, and MYC) through regulating the HIF and TNF signaling pathways, which were validated using the RT-qPCR, immunofluorescence staining and western blotting assays. In conclusion, the present study systematically demonstrated the possible therapeutic effects and mechanisms of Lycium barbarum on oral mucositis through network pharmacology analysis and experimental validation. The results showed that Lycium barbarum could promote healing and reduce the inflammatory response through TNF and HIF signaling pathways.

Optimized differential evolution and hybrid deep learning for superior drug-target binding affinity prediction

Investigating Drug-Target Interactions (DTI) is crucial for drug repositioning and discovery tasks. However, discovering DTIs through experimental approaches is time-consuming and requires substantial financial resources. To address these challenges, machine learning-based methodologies have been adopted to reduce costs and save time. Unfortunately, the effectiveness of these methods has been limited due to the binary classification approach and the lack of empirically validated negative samples. The availability of abundant DTI datasets and protein structure data has enabled the development of new approaches, such as redefining the DTI problem as a regression task. Given this context, we propose an innovative deep-learning approach to predict binding affinities between drugs and targets. Our model, named the Convolution Self-Attention Network with Attention-based Bidirectional Long Short-Term Memory Network (CSAN-BiLSTM-Att), integrates convolutional neural network (CNN) blocks with self-attention mechanisms to create an attention-based bidirectional long short-term memory (BiLSTM) model, followed by fully connected layers. Due to the model's complexity, proper hyperparameter tuning is essential. To optimize this, we employ the Differential Evolution (DE) technique to select the most suitable hyperparameters. Experimental results demonstrate that the DE-based CSAN-BiLSTM-Att model outperforms previous approaches. Specifically, the model achieved a concordance index of 0.898 and a mean square error of 0.228 on the DAVIS dataset, and a concordance value of 0.971 with a mean square error of 0.014 on the KIBA dataset.

MFF-DTA: Multi-scale feature fusion for drug-target affinity prediction

Accurately predicting drug-target affinity is crucial in expediting the discovery and development of new drugs, which is a complex and risky process. Identifying these interactions not only aids in screening potential compounds but also guides further optimization. To address this, we propose a multi-perspective feature fusion model, MFF-DTA, which integrates chemical structure, biological sequence, and other data to comprehensively capture drug-target affinity features. The MFF-DTA model incorporates multiple feature learning components, each of which is capable of extracting drug molecular features and protein target information, respectively. These components are able to obtain key information from both global and local perspectives. Then, these features from different perspectives are efficiently combined using specific splicing strategies to create a comprehensive representation. Finally, the model uses the fused features to predict drug-target affinity. Comparative experiments show that MFF-DTA performs optimally on the Davis and KIBA data sets. Ablation experiments demonstrate that removing specific components results in the loss of unique information, thus confirming the effectiveness of the MFF-DTA design. Improvements in DTA prediction methods will decrease costs and time in drug development, enhancing industry efficiency and ultimately benefiting patients.

From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning.

Accurate prediction of protein-ligand binding affinities is an essential challenge in structure-based drug design. Despite recent advances in data-driven methods for affinity prediction, their accuracy is still limited, partially because they only take advantage of static crystal structures while the actual binding affinities are generally determined by the thermodynamic ensembles between proteins and ligands. One effective way to approximate such a thermodynamic ensemble is to use molecular dynamics (MD) simulation. Here, an MD dataset containing 3,218 different protein-ligand complexes is curated, and Dynaformer, a graph-based deep learning model is further developed to predict the binding affinities by learning the geometric characteristics of the protein-ligand interactions from the MD trajectories. In silico experiments demonstrated that the model exhibits state-of-the-art scoring and ranking power on the CASF-2016 benchmark dataset, outperforming the methods hitherto reported. Moreover, in a virtual screening on heat shock protein 90 (HSP90) using Dynaformer, 20 candidates are identified and their binding affinities are further experimentally validated. Dynaformer displayed promising results in virtual drug screening, revealing 12 hit compounds (two are in the submicromolar range), including several novel scaffolds. Overall, these results demonstrated that the approach offer a promising avenue for accelerating the early drug discoveryprocess.

MMD-DTA: A multi-modal deep learning framework for drug-target binding affinity and binding region prediction.

The prediction of drug-target affinity (DTA) plays a crucial role in drug development and the identification of potential drug targets. In recent years, computer-assisted DTA prediction has emerged as a significant approach in this field. In this study, we propose a multi-modal deep learning framework called MMD-DTA for predicting drug-target binding affinity and binding regions. The model can predict DTA while simultaneously learning the binding regions of drug-target interactions through unsupervised learning. To achieve this, MMD-DTA first uses graph neural networks and target structural feature extraction network to extract multi-modal information from the sequences and structures of drugs and targets. It then utilizes the feature interaction and fusion modules to generate interaction descriptors for predicting DTA and interaction strength for binding region prediction. Our experimental results demonstrate that MMD-DTA outperforms existing models based on key evaluation metrics. Furthermore, external validation results indicate that MMD-DTA enhances the generalization capability of the model by integrating sequence and structural information of drugs and targets. The model trained on the benchmark dataset can effectively generalize to independent virtual screening tasks. The visualization of drug-target binding region prediction showcases the interpretability of MMD-DTA, providing valuable insights into the functional regions of drug molecules that interact with proteins.

MvGraphDTA: multi-view-based graph deep model for drug-target affinity prediction by introducing the graphs and line graphs

BackgroundAccurately identifying drug-target affinity (DTA) plays a pivotal role in drug screening, design, and repurposing in pharmaceutical industry. It not only reduces the time, labor, and economic costs associated with biological experiments but also expedites drug development process. However, achieving the desired level of computational accuracy for DTA identification methods remains a significant challenge.ResultsWe proposed a novel multi-view-based graph deep model known as MvGraphDTA for DTA prediction. MvGraphDTA employed a graph convolutional network (GCN) to extract the structural features from original graphs of drugs and targets, respectively. It went a step further by constructing line graphs with edges as vertices based on original graphs of drugs and targets. GCN was also used to extract the relationship features within their line graphs. To enhance the complementarity between the extracted features from original graphs and line graphs, MvGraphDTA fused the extracted multi-view features of drugs and targets, respectively. Finally, these fused features were concatenated and passed through a fully connected (FC) network to predict DTA.ConclusionsDuring the experiments, we performed data augmentation on all the training sets used. Experimental results showed that MvGraphDTA outperformed the competitive state-of-the-art methods on benchmark datasets for DTA prediction. Additionally, we evaluated the universality and generalization performance of MvGraphDTA on additional datasets. Experimental outcomes revealed that MvGraphDTA exhibited good universality and generalization capability, making it a reliable tool for drug-target interaction prediction.

Innovative Mamba and graph transformer framework for superior protein-ligand affinity prediction

Accurate measurement of the affinity between drug targets is of great importance in the field of drug discovery, as it provides significant information about drug action. However, the diverse representations of compounds and proteins increase the difficulty of this task. Recently, numerous studies have explored the application of various deep learning methods and architectures in Drug-Target Affinity (DTA) prediction tasks, continuously improving performance. In this work, we present a multimodal late-stage fusion prediction model called MGDTA that combines the pioneering Mamba architecture with a hybrid model architecture based on graph transformers and transformers (GTT). For molecular compounds, structural characterisation is extracted using GTT layers. Additionally, a feature integration module is utilized to obtain molecular features from derived molecular fingerprints, culminating in a unified representation of drug molecules through a feature integration module. For proteins, we adopt the state-space model (SSM) module Mamba to learn feature representations from target protein sequences. This represents a preliminary exploration of the Mamba module in protein sequence feature extraction in DTA tasks. Finally, we predict the results by integrating the feature representations of both molecules and proteins. Our results indicate that the proposed model achieves superior performance on benchmark datasets, validating the feasibility of using the Mamba architecture for DTA tasks. Furthermore, sequence feature visualization demonstrates the capability of the Mamba block to focus on binding site residues within drug targets.

Identification of Peregrin inhibitors-modulators by harnessing the computational prowess of molecular simulation and machine learning algorithms

Peregrin is marked as a potential drug target due to its pivotal role in the epigenetic maintenance of cellular metabolism. It is counted among those of the bromodomain-containing protein family. The protein binds to the acetylated lysine of N-terminus histone proteins of the nucleosomes. This bound form neutralizes the otherwise positively charged histone and, thus, crucially impacts the DNA replication mechanism. In several diseases like cancer, bone loss, and leukemia, this peregrin binding impacts the genes’ overexpression. Hence, the present study, computational screening of the bromodomain-specific inhibitors from a library of 5430 compounds retrieved from the ChemDiv database. Two different scoring functions based molecular docking studies have been employed for molecular interaction analysis. Followed by an artificial intelligence-based absolute binding affinity prediction has been confirmed through KDeep, which follows a machine learning step to obtain a precise binding energy score for the set of best compounds in the screening process. The resultant compounds were then screened for pharmacokinetics and drug-likeliness properties. Using molecular dynamics (MD) simulation analysis, the dynamic behavior of the four suggested compounds and Peregrin has been investigated for a 100 ns period. Identified compounds are found to be sufficiently effective in holding their strong interaction affinity toward Peregrin for an adequate time span, and structural integrity is also found to be comparable with the standard compound considered in the study. Mostly, all four potent compounds (K788-9421, S357-0084, S357-0893, S357-0915) are hydrophobic and evaluated for thermodynamics property analysis using MM-GBSA and free energy landscape (FEL) calculations. However, among all, K788-9421 shows comparatively better and energetically most favorable interactions with peregrin. Overall, study findings can lead to further research in developing next-generation broad-spectrum Peregrin inhibitors-modulators; they need extensive experimental validation for considering the compounds for clinical-trial application for being an excellent drug-candidate entity against Peregrin protein.

Machine Learning Guided AQFEP: A Fast and Efficient Absolute Free Energy Perturbation Solution for Virtual Screening.

Structure-based methods in drug discovery have become an integral part of the modern drug discovery process. The power of virtual screening lies in its ability to rapidly and cost-effectively explore enormous chemical spaces to select promising ligands for further experimental investigation. Relative free energy perturbation (RFEP) and similar methods are the gold standard for binding affinity prediction in drug discovery hit-to-lead and lead optimization phases, but have high computational cost and the requirement of a structural analog with a known activity. Without a reference molecule requirement, absolute FEP (AFEP) has, in theory, better accuracy for hit ID, but in practice, the slow throughput is not compatible with VS, where fast docking and unreliable scoring functions are still the standard. Here, we present an integrated workflow to virtually screen large and diverse chemical libraries efficiently, combining active learning with a physics-based scoring function based on a fast absolute free energy perturbation method. We validated the performance of the approach in the ranking of structurally related ligands, virtual screening hit rate enrichment, and active learning chemical space exploration; disclosing the largest reported collection of free energy simulations to date.

Accelerated Discovery of Carbamate Cbl-b Inhibitors Using Generative AI Models and Structure-Based Drug Design.

Casitas B-lymphoma proto-oncogene-b (Cbl-b) is a RING finger E3 ligase that has an important role in effector T cell function, acting as a negative regulator of T cell, natural killer (NK) cell, and B cell activation. A discovery effort toward Cbl-b inhibitors was pursued in which a generative AI design engine, REINVENT, was combined with a medicinal chemistry structure-based design to discover novel inhibitors of Cbl-b. Key to the success of this effort was the evolution of the "Design" phase of the Design-Make-Test-Analyze cycle to involve iterative rounds of an in silico structure-based drug design, strongly guided by physics-based affinity prediction and machine learning DMPK predictive models, prior to selection for synthesis. This led to the accelerated discovery of a potent series of carbamate Cbl-b inhibitors.

GraphCL-DTA: A Graph Contrastive Learning With Molecular Semantics for Drug-Target Binding Affinity Prediction.

Drug-target binding affinity prediction plays an important role in the early stages of drug discovery, which can infer the strength of interactions between new drugs and new targets. However, the performance of previous computational models is limited by the following drawbacks. The learning of drug representation relies only on supervised data without considering the information in the molecular graph itself. Moreover, most previous studies tended to design complicated representation learning modules, while uniformity used to measure representation quality is ignored. In this study, we propose GraphCL-DTA, a graph contrastive learning with molecular semantics for drug-target binding affinity prediction. This graph contrastive learning framework replaces the dropout-based data augmentation strategy by performing data augmentation in the embedding space, thereby better preserving the semantic information of the molecular graph. A more essential and effective drug representation can be learned through this graph contrastive framework without additional supervised data. Next, we design a new loss function that can be directly used to adjust the uniformity of drug and target representations. By directly optimizing the uniformity of representations, the representation quality of drugs and targets can be improved. The effectiveness of the above innovative elements is verified on two real datasets, KIBA and Davis. Compared with the GraphDTA model, the relative improvement of the GraphCL-DTA model on the two datasets is 2.7% and 4.5%. The graph contrastive learning framework and uniformity function in the GraphCL-DTA model can be embedded into other computational models as independent modules to improve their generalization capability.

Hybrid Quantum Neural Network Approaches to Protein–Ligand Binding Affinity Prediction

Drug repositioning is a less expensive and time-consuming method than the traditional method of drug discovery. It is a strategy for identifying new uses for approved or investigational drugs that are outside the scope of the original medical indication. A key strategy in repositioning approved or investigational drugs is determining the binding affinity of these drugs to target proteins. The large increase in available experimental data has helped deep learning methods to demonstrate superior performance compared to conventional prediction and other traditional computational methods in precise binding affinity prediction. However, these methods are complex and time-consuming, presenting a significant barrier to their development and practical application. In this context, quantum computing (QC) and quantum machine learning (QML) theoretically offer promising solutions to effectively address these challenges. In this work, we introduce a hybrid quantum–classical framework to predict binding affinity. Our approach involves, initially, the implementation of an efficient classical model using convolutional neural networks (CNNs) for feature extraction and three fully connected layers for prediction. Subsequently, retaining the classical module for feature extraction, we implement various quantum and classical modules for binding affinity prediction, which accept the concatenated features as input. Quantum predicted modules are implemented with Variational Quantum Regressions (VQRs), while classical predicted modules are implemented with various fully connected layers. Our findings clearly show that hybrid quantum–classical models accelerate the training process in terms of epochs and achieve faster stabilization. Also, these models demonstrate quantum superiority in terms of complexity, accuracy, and generalization, thereby indicating a promising direction for QML.

Fully Flexible Molecular Alignment Enables Accurate Ligand Structure Modeling.

Accurate protein-ligand binding poses are the prerequisites of structure-based binding affinity prediction and provide the structural basis for in-depth lead optimization in small molecule drug design. However, it is challenging to provide reasonable predictions of binding poses for different molecules due to the complexity and diversity of the chemical space of small molecules. Similarity-based molecular alignment techniques can effectively narrow the search range, as structurally similar molecules are likely to have similar binding modes, with higher similarity usually correlated to higher success rates. However, molecular similarity is not consistently high because molecules often require changes to achieve specific purposes, leading to reduced alignment precision. To address this issue, we propose a new alignment method─Z-align. This method uses topological structural information as a criterion for evaluating similarity, reducing the reliance on molecular fingerprint similarity. Our method has achieved success rates significantly higher than those of other methods at moderate levels of similarity. Additionally, our approach can comprehensively and flexibly optimize bond lengths and angles of molecules, maintaining a high accuracy even when dealing with larger molecules. Consequently, our proposed solution helps in achieving more accurate binding poses in protein-ligand docking problems, facilitating the development of small molecule drugs. Z-align is freely available as a web server at https://cloud.zelixir.com/zalign/home.