Attention is all you need: utilizing attention in AI-enabled drug discovery

Briefings in Bioinformatics, Journal Year: 2023, Volume and Issue: 25(1)

Published: Nov. 22, 2023

Abstract Recently, attention mechanism and derived models have gained significant traction in drug development due to their outstanding performance interpretability handling complex data structures. This review offers an in-depth exploration of the principles underlying attention-based advantages discovery. We further elaborate on applications various aspects development, from molecular screening target binding property prediction molecule generation. Finally, we discuss current challenges faced application mechanisms Artificial Intelligence technologies, including quality, model computational resource constraints, along with future directions for research. Given accelerating pace technological advancement, believe that will increasingly prominent role anticipate these usher revolutionary breakthroughs pharmaceutical domain, significantly development.

Language: Английский

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Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities

Frontiers in Pharmacology, Journal Year: 2024, Volume and Issue: 15

Published: Feb. 7, 2024

There are two main ways to discover or design small drug molecules. The first involves fine-tuning existing molecules commercially successful drugs through quantitative structure-activity relationships and virtual screening. second approach generating new de novo inverse relationship. Both methods aim get a molecule with the best pharmacokinetic pharmacodynamic profiles. However, bringing market is an expensive time-consuming endeavor, average cost being estimated at around $2.5 billion. One of biggest challenges screening vast number potential candidates find one that both safe effective. development artificial intelligence in recent years has been phenomenal, ushering revolution many fields. field pharmaceutical sciences also significantly benefited from multiple applications intelligence, especially discovery projects. Artificial models finding use molecular property prediction, generation, screening, synthesis planning, repurposing, among others. Lately, generative gained popularity across domains for its ability generate entirely data, such as images, sentences, audios, videos, novel chemical molecules, etc. Generative delivered promising results development. This review article delves into fundamentals framework various context via approach. Various basic advanced have discussed, along their applications. explores examples advances approach, well ongoing efforts fully harness faster more affordable manner. Some clinical-level assets generated form discussed this show ever-increasing application commercial partnerships.

Language: Английский

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CFSSynergy: Combining Feature-Based and Similarity-Based Methods for Drug Synergy Prediction

Journal of Chemical Information and Modeling, Journal Year: 2024, Volume and Issue: 64(7), P. 2577 - 2585

Published: March 22, 2024

Drug synergy prediction plays a vital role in cancer treatment. Because experimental approaches are labor-intensive and expensive, computational-based get more attention. There two types of computational methods for drug prediction: feature-based similarity-based. In methods, the main focus is to extract discriminative features from pairs cell lines pass task predictor. similarity-based similarities among all drugs utilized as fed into this work, novel approach, called CFSSynergy, that combines these viewpoints proposed. First, representation extracted paired input. We have transformer-based architecture drugs. For lines, we created similarity matrix between proteins using Node2Vec algorithm. Then, new line computed by multiplying protein–protein initial representation. Next, compute unique cells learned lines. based on features. Finally, XGBoost Two well-known data sets were used evaluate performance our proposed method: DrugCombDB OncologyScreen. The CFSSynergy approach consistently outperformed existing comparative evaluations. This substantiates efficacy capturing complex synergistic interactions setting it apart conventional or methods.

Language: Английский

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AI for targeted polypharmacology: The next frontier in drug discovery

Current Opinion in Structural Biology, Journal Year: 2024, Volume and Issue: 84, P. 102771 - 102771

Published: Jan. 11, 2024

Language: Английский

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Using transformers for multimodal emotion recognition: Taxonomies and state of the art review

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 133, P. 108339 - 108339

Published: April 2, 2024

Language: Английский

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Innovative super-resolution in spatial transcriptomics: a transformer model exploiting histology images and spatial gene expression

Briefings in Bioinformatics, Journal Year: 2024, Volume and Issue: 25(2)

Published: Jan. 22, 2024

Abstract Spatial transcriptomics technologies have shed light on the complexities of tissue structures by accurately mapping spatial microenvironments. Nonetheless, a myriad methods, especially those utilized in platforms like Visium, often relinquish details owing to intrinsic resolution limitations. In response, we introduce TransformerST, an innovative, unsupervised model anchored Transformer architecture, which operates independently references, thereby ensuring cost-efficiency circumventing need for single-cell RNA sequencing. TransformerST not only elevates Visium data from multicellular level granularity but also showcases adaptability across diverse platforms. By employing vision transformer-based encoder, it discerns latent image-gene expression co-representations and is further enhanced correlations, derived adaptive graph module. The sophisticated cross-scale network, super-resolution, significantly boosts model’s accuracy, unveiling complex structure–functional relationships within histology images. Empirical evaluations validate its adeptness revealing subtleties at scale. Crucially, adeptly navigates through co-representation, maximizing synergistic utility gene images, emerging as pioneering tool transcriptomics. It enhances introduces novel approach that optimally utilizes images alongside expression, providing refined lens investigating

Language: Английский

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Systematic prediction of synergistic drug combinations through network-based deep learning framework

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 126566 - 126566

Published: Jan. 1, 2025

Language: Английский

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DeepCompoundNet: enhancing compound–protein interaction prediction with multimodal convolutional neural networks

Journal of Biomolecular Structure and Dynamics, Journal Year: 2023, Volume and Issue: unknown, P. 1 - 10

Published: Dec. 12, 2023

Virtual screening has emerged as a valuable computational tool for predicting compound-protein interactions, offering cost-effective and rapid approach to identifying potential candidate drug molecules. Current machine learning-based methods rely on molecular structures their relationship in the network. The former utilizes information such amino acid sequences chemical structures, while latter leverages interaction network data, protein-protein drug-disease protein-disease interactions. However, there been limited exploration of integrating with networks. This study presents DeepCompoundNet, deep model that integrates protein features, properties, diverse data predict chemical-protein DeepCompoundNet outperforms state-of-the-art prediction, demonstrated through performance evaluations. Our findings highlight complementary nature multiple extending beyond sequence homology structure similarity. Moreover, our model's analysis confirms gets higher interactions between proteins chemicals not observed training samples.Communicated by Ramaswamy H. Sarma.

Language: Английский

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PLANNER: A Multi-Scale Deep Language Model for the Origins of Replication Site Prediction

IEEE Journal of Biomedical and Health Informatics, Journal Year: 2024, Volume and Issue: 28(4), P. 2445 - 2454

Published: Jan. 4, 2024

Origins of replication sites (ORIs) are crucial genomic regions where DNA initiation takes place, playing pivotal roles in fundamental biological processes like cell division, gene expression regulation, and integrity. Accurate identification ORIs is essential for comprehending replication, expression, mutation-related diseases. However, experimental approaches ORI often expensive time-consuming, leading to the growing popularity computational methods. In this study, we present PLANNER (DeeP LeArNiNg prEdictor ORI), a novel approach species-specific cell-specific prediction eukaryotic ORIs. uses multi-scale k-tuple sequences as input employs DNABERT pre-training model with transfer learning ensemble strategies train accurate predictive models. Extensive empirical test results demonstrate that achieved superior performance compared state-of-the-art approaches, including iOri-Euk, Stack-ORI, ORI-Deep, within specific types across different types. Furthermore, by incorporating an interpretable analysis mechanism, provide insights into learned patterns, facilitating mapping from discovering important sequential determinants comprehensively analysing their functions.

Language: Английский

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scMMT: a multi-use deep learning approach for cell annotation, protein prediction and embedding in single-cell RNA-seq data

Briefings in Bioinformatics, Journal Year: 2024, Volume and Issue: 25(2)

Published: Jan. 22, 2024

Abstract Accurate cell type annotation in single-cell RNA-sequencing data is essential for advancing biological and medical research, particularly understanding disease progression tumor microenvironments. However, existing methods are constrained by single feature extraction approaches, lack of adaptability to immune types with similar molecular profiles but distinct functions a failure account the impact label noise on model accuracy, all which compromise precision annotation. To address these challenges, we developed supervised approach called scMMT. We proposed novel technique uncover more valuable information. Additionally, constructed multi-task learning framework based GradNorm method enhance recognition challenging cells reduce facilitating mutual reinforcement between protein prediction tasks. Furthermore, introduced logarithmic weighting smoothing mechanisms ability rare prevent overconfidence. Through comprehensive evaluations multiple public datasets, scMMT has demonstrated state-of-the-art performance various aspects including annotation, identification, dropout resistance, expression low-dimensional embedding representation.

Language: Английский

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