Generative Pre-trained Transformer (GPT) based model with relative attention for de novo drug design.

The complex molecular mechanism and pathophysiology of Alzheimer's disease (AD) limits the development of effective therapeutics or prevention strategies. Artificial Intelligence (AI)-guided drug discovery combined with genetics/multi-omics (genomics, epigenomics, transcriptomics, proteomics, and metabolomics) analysis contributes to the understanding of the pathophysiology and precision medicine of the disease, including AD and AD-related dementia. In this review, we summarize the AI-driven methodologies for AD-agnostic drug discovery and development, including de novo drug design, virtual screening, and prediction of drug-target interactions, all of which have shown potentials. In particular, AI-based drug repurposing emerges as a compelling strategy to identify new indications for existing drugs for AD. We provide several emerging AD targets from human genetics and multi-omics findings and highlight recent AI-based technologies and their applications in drug discovery using AD as a prototypical example. In closing, we discuss future challenges and directions in AI-based drug discovery for AD and other neurodegenerative diseases. 

Artificial intelligence for drug discovery and development in Alzheimer's disease.

The rational modification of siRNA molecules is crucial for ensuring their drug-like properties. Machine learning-based prediction of chemically modified siRNA (cm-siRNA) efficiency can significantly optimize the design process of siRNA chemical modifications, saving time and cost in siRNA drug development. However, existing in-silico methods suffer from limitations such as small datasets, inadequate data representation capabilities, and lack of interpretability. Therefore, in this study, we developed the Cm-siRPred algorithm based on a multi-view learning strategy. The algorithm employs a multi-view strategy to represent the double-strand sequences, chemical modifications, and physicochemical properties of cm-siRNA. It incorporates a cross-attention model to globally correlate different representation vectors and a two-layer CNN module to learn local correlation features. The algorithm demonstrates exceptional performance in cross-validation experiments, independent dataset, and case studies on approved siRNA drugs, and showcasing its robustness and generalization ability. In addition, we developed a user-friendly webserver that enables efficient prediction of cm-siRNA efficiency and assists in the design of siRNA drug chemical modifications. In summary, Cm-siRPred is a practical tool that offers valuable technical support for siRNA chemical modification and drug efficiency research, while effectively assisting in the development of novel small nucleic acid drugs. Cm-siRPred is freely available at https://cellknowledge.com.cn/sirnapredictor/. 

Cm-siRPred: Predicting chemically modified siRNA efficiency based on multi-view learning strategy.

Abstract Generative deep learning is reshaping drug design. Chemical language models (CLMs) – which generate molecules in the form of molecular strings – bear particular promise for this endeavor. Here, we introduce a recent deep learning architecture, termed Structured State Space Sequence (S4) model, into de novo drug design. In addition to its unprecedented performance in various fields, S4 has shown remarkable capabilities to learn the global properties of sequences. This aspect is intriguing in chemical language modeling, where complex molecular properties like bioactivity can ‘emerge’ from separated portions in the molecular string. This observation gives rise to the following question: Can S4 advance chemical language modeling for de novo design? To provide an answer, we systematically benchmark S4 with state-of-the-art CLMs on an array of drug discovery tasks, such as the identification of bioactive compounds, and the design of drug-like molecules and natural products. S4 shows a superior capacity to learn complex molecular properties, while at the same time exploring diverse scaffolds. Finally, when applied prospectively to kinase inhibition, S4 designs eight of out ten molecules that are predicted as highly active by molecular dynamics simulations. Taken together, these findings advocate for the introduction of S4 into chemical language modeling – uncovering its untapped potential in the molecular sciences. 

Chemical language modeling with structured state space sequence models

Transformer models have emerged as pivotal tools within the realm of drug discovery, distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes. Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data, these models showcase remarkable efficacy across various tasks, including new drug design and drug target identification. The adaptability of pre-trained transformer-based models renders them indispensable assets for driving data-centric advancements in drug discovery, chemistry, and biology, furnishing a robust framework that expedites innovation and discovery within these domains. Beyond their technical prowess, the success of transformer-based models in drug discovery, chemistry, and biology extends to their interdisciplinary potential, seamlessly combining biological, physical, chemical, and pharmacological insights to bridge gaps across diverse disciplines. This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields. In our review, we elucidate the myriad applications of transformers in drug discovery, as well as chemistry and biology, spanning from protein design and protein engineering, to molecular dynamics (MD), drug target identification, transformer-enabled drug virtual screening (VS), drug lead optimization, drug addiction, small data set challenges, chemical and biological image analysis, chemical language understanding, and single cell data. Finally, we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.

A review of transformers in drug discovery and beyond

Drug discovery for a protein target is a laborious and costly process. Deep learning (DL) methods have been applied to drug discovery and successfully generated novel molecular structures, and they can substantially reduce development time and costs. However, most of them rely on prior knowledge, either by drawing on the structure and properties of known molecules to generate similar candidate molecules or extracting information on the binding sites of protein pockets to obtain molecules that can bind to them. In this paper, DeepTarget, an end-to-end DL model, was proposed to generate novel molecules solely relying on the amino acid sequence of the target protein to reduce the heavy reliance on prior knowledge. DeepTarget includes three modules: Amino Acid Sequence Embedding (AASE), Structural Feature Inference (SFI), and Molecule Generation (MG). AASE generates embeddings from the amino acid sequence of the target protein. SFI inferences the potential structural features of the synthesized molecule, and MG seeks to construct the eventual molecule. The validity of the generated molecules was demonstrated by a benchmark platform of molecular generation models. The interaction between the generated molecules and the target proteins was also verified on the basis of two metrics, drug-target affinity and molecular docking. The results of the experiments indicated the efficacy of the model for direct molecule generation solely conditioned on amino acid sequence. 

/pdf/deep-generative-model-for-drug-design-from-protein-target-1od2uadi.pdf

Deep generative model for drug design from protein target sequence

Image-based generation for molecule design with SketchMol

LRBmat: A novel gut microbial interaction and individual heterogeneity inference method for colorectal cancer.



The application of deep generative models for molecular discovery has
witnessed a significant surge in recent years. Currently, the field of molecular generation and molecular
optimization is predominantly governed by autoregressive models regardless of how molecular
data is represented. However, an emerging paradigm in the generation domain is diffusion models,
which treat data non-autoregressively and have achieved significant breakthroughs in areas such
as image generation.



The potential and capability of diffusion models in molecular generation and optimization
tasks remain largely unexplored. In order to investigate the potential applicability of diffusion models
in the domain of molecular exploration, we proposed DiffSeqMol, a molecular sequence generation
model, underpinned by diffusion process.



DiffSeqMol distinguishes itself from traditional autoregressive methods by
its capacity to draw samples from random noise and direct generating the entire molecule. Through
experiment evaluations, we demonstrated that DiffSeqMol can achieve, even surpass, the performance
of established state-of-the-art models on unconditional generation tasks and molecular optimization
tasks.



Taken together, our results show that DiffSeqMol can be considered a promising molecular
generation method. It opens new pathways to traverse the expansive chemical space and to
discover novel molecules.


DiffSeqMol: A Non-Autoregressive Diffusion-Based Approach for Molecular Sequence Generation and Optimization

Protein-protein interactions (PPIs) play a crucial role in many biochemical processes and biological processes. Recently, many structure-based molecular generative models have been proposed. However, PPI sites and compounds targeting PPIs have distinguished physicochemical properties compared to traditional binding pockets and drugs, it is still a challenging task to generate compounds targeting PPIs by considering PPI complexes or interface hotspot residues. In this work, we propose a specifically molecular generative framework based on PPI interfaces, named GENiPPI. We evaluated the framework and found it can capture the implicit relationship between the PPI interface and the active molecules, and can generate novel compounds that target the PPI interface. Furthermore, the framework is able to generate diverse novel compounds with limited PPI interface inhibitors. The results show that PPI interface-based molecular generative model enriches structure-based molecular generative models and facilitates the design of inhibitors based on PPI structures.

An interface-based molecular generative framework for protein-protein interaction inhibitors

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