Identifying synergistic drug combinations is paramount significance in addressing complex diseases while reducing the risks of toxicities and other adverse effects. Although a plethora of computational methods have be...
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作者：
Srinivasan, SanjanaBajorath, JuergenUniv Bonn
Dept Life Sci Informat & Data Sci B IT Friedrich-Hirzebruch-Allee 5-6 D-53115 Bonn Germany Univ Bonn
Lamarr Inst Machine Learning & Artificial Intellig Friedrich-Hirzebruch-Allee 5-6 D-53115 Bonn Germany Univ Bonn
Limes Inst Program Unit Chem Biol & Med Chem Friedrich-Hirzebruch-Allee 5-6 D-53115 Bonn Germany
Compounds with defined multi-target activity are candidates for the treatment of multi-factorial diseases. Such compounds are mostly discovered experimentally. Designing compounds with the desired activity against two...
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Compounds with defined multi-target activity are candidates for the treatment of multi-factorial diseases. Such compounds are mostly discovered experimentally. Designing compounds with the desired activity against two targets is typically attempted by pharmacophore fusion. In addition, machine learning models can be derived for multi-target prediction of compounds or computational target profiling. Here, we introduce transformer-based chemical language model variants for the generative design of dual-target compounds. Alternative models were pre-trained by learning mappings of single- to dual-target compounds of increasing similarity. Different models were optimized for generating compounds with activity against pairs of functionally unrelated targets using a new cross-fine-tuning approach. Control models confirmed that pre-trained and finetuned models learned the chemical space of dual-target compounds. The final models were found to exactly reproduce known dual-target compounds excluded from model derivation. In addition, many structural analogs of such compounds were generated, further supporting the validity of the methodology.
Natural products are substances produced by organisms in nature and often possess biological activity and structural diversity. Drug development based on natural products has been common for many years. However, the i...
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Natural products are substances produced by organisms in nature and often possess biological activity and structural diversity. Drug development based on natural products has been common for many years. However, the intricate structures of these compounds present challenges in terms of structure determination and synthesis, particularly compared to the efficiency of high-throughput screening of synthetic compounds. In recent years, deep learning-based methods have been applied to the generation of molecules. In this study, we trained chemical language models on a natural product dataset and generated natural product-like compounds and verified the performance of the generated compounds as a drug candidate library. The results showed that the distribution of the compounds generated was similar to that of natural products. We also evaluated the effectiveness of the generated compounds as drug candidates. Our method can be used to explore the vast chemical space and reduce the time and cost of drug discovery of natural products.
Generative deep learning is accelerating de novo drug design, by allowing the generation of molecules with desired properties on demand. chemical language models - which generate new molecules in the form of strings u...
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Generative deep learning is accelerating de novo drug design, by allowing the generation of molecules with desired properties on demand. chemical language models - which generate new molecules in the form of strings using deep learning - have been particularly successful in this endeavour. Thanks to ad-vances in natural language processing methods and interdis-ciplinary collaborations, chemical language models are expected to become increasingly relevant in drug discovery. This minireview provides an overview of the current state-of-the-art of chemical language models for de novo design, and analyses current limitations, challenges, and advantages. Finally, a perspective on future opportunities is provided.
Natural products are substances produced by organisms in nature and often possess biological activity and structural diversity. Drug development based on natural products has been common for many years. However, the i...
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With the continuous development of artificial intelligence technology, more and more computational models for generating new molecules are being developed. However, we are often confronted with the question of whether...
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With the continuous development of artificial intelligence technology, more and more computational models for generating new molecules are being developed. However, we are often confronted with the question of whether these compounds are easy or difficult to synthesize, which refers to synthetic accessibility of compounds. In this study, a deep learning based computational model called DeepSA, was proposed to predict the synthesis accessibility of compounds, which provides a useful tool to choose molecules. DeepSA is a chemical language model that was developed by training on a dataset of 3,593,053 molecules using various natural language processing (NLP) algorithms, offering advantages over state-of-the-art methods and having a much higher area under the receiver operating characteristic curve (AUROC), i.e., 89.6%, in discriminating those molecules that are difficult to synthesize. This helps users select less expensive molecules for synthesis, reducing the time and cost required for drug discovery and development. Interestingly, a comparison of DeepSA with a Graph Attention-based method shows that using SMILES alone can also efficiently visualize and extract compound's informative features. DeepSA is available online on the below web server (https://***/services/deepsa/) of our group, and the code is available at https://***/Shihang-Wang-58/DeepSA.
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