Comparison study of dominant molecular sequence representation based on diffusion model

Wouters OJ, McKee M, Luyten J (2020) Estimated research and development investment needed to bring a new medicine to market, 2009–2018. JAMA 323(9):844–853

PubMed  PubMed Central  Google Scholar 

Polishchuk PG, Madzhidov TI, Varnek A (2013) Estimation of the size of drug-like chemical space based on GDB-17 data. J Comput Aided Mol Des 27:675–679

CAS  PubMed  Google Scholar 

Hert J et al (2009) Quantifying biogenic bias in screening libraries. Nat Chem Biol 5(7):479–483

CAS  PubMed  PubMed Central  Google Scholar 

Shao H et al (2020) Controlvae: Controllable variational autoencoder. in International conference on machine learning. PMLR

Song L et al (2021) Structural information preserving for graph-to-text generation. arXiv preprint arXiv:2102.06749

Balaji Y et al (2019) Conditional GAN with Discriminative Filter Generation for Text-to-Video Synthesis. in IJCAI

Xue D et al (2019) Advances and challenges in deep generative models for de Novo molecule generation. Wiley Interdisciplinary Reviews: Comput Mol Sci 9(3):e1395

Google Scholar 

Kingma DP, Welling M (2013) Auto-Encoding Variational Bayes CoRR, abs/1312.6114.

Gómez-Bombarelli R et al (2018) Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent Sci 4(2):268–276

PubMed  PubMed Central  Google Scholar 

Cai Z et al (2021) Generative adversarial networks. ACM Computing Surveys (CSUR)

Jabbar R, Jabbar R, Kamoun S (2022) Recent progress in generative adversarial networks applied to inversely designing inorganic materials: A brief review. Comput Mater Sci 213:111612

CAS  Google Scholar 

Studies from Carnegie Mellon University Have Provided New Information about Chemicals and Chemistry (Generative and Reinforcement Learning Approaches for the Automated De Novo Design of Bioactive Compounds). Network Daily News, 2022(16): pp. 49–50

He J et al (2022) Transformer-based molecular optimization beyond matched molecular pairs. J Cheminform 14(1):18

CAS  PubMed  PubMed Central  Google Scholar 

Ross J et al (2022) Large-scale chemical Language representations capture molecular structure and properties. Nat Mach Intell 4(12):1256–1264

Google Scholar 

Shi C et al (2020) Graphaf: a flow-based autoregressive model for molecular graph generation. arXiv preprint arXiv:2001.09382

Yang L et al (2023) Diffusion models: A comprehensive survey of methods and applications. ACM-CSUR 56(4):1–39

Google Scholar 

Rombach R et al (2022) High-resolution image synthesis with latent diffusion models. in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Kong Z et al (2020) Diffwave: A versatile diffusion model for audio synthesis. arXiv preprint arXiv:2009.09761

Ho J et al (2022) Video diffusion models. Adv Neural Inf Process Syst 35:8633–8646

Google Scholar 

Hoogeboom E et al (2022) Equivariant diffusion for molecule generation in 3d. in International conference on machine learning. PMLR

Xu M et al (2023) Geometric latent diffusion models for 3d molecule generation. in International Conference on Machine Learning. PMLR

Luu RK, Marcin W, Buehler MJ (2023) Generative discovery of de Novo chemical designs using diffusion modeling and transformer deep neural networks with application to deep eutectic solvents. Appl Phys Lett, 122(23)

Wang S et al (2019) Smiles-bert: large scale unsupervised pre-training for molecular property prediction. in Proceedings of the 10th ACM international conference on bioinformatics, computational biology and health informatics

Edwards W, Barron FH (1994) SMARTS and SMARTER: improved simple methods for multiattribute utility measurement. Organ Behav Hum Decis Process 60(3):306–325

Google Scholar 

Rajan K, Zielesny A, Steinbeck C STOUT: SMILES to IUPAC names using neural machine translation. J Cheminform (2021); 13: 34

CAS  PubMed  PubMed Central  Google Scholar 

Krenn M et al (2022) SELFIES and the future of molecular string representations. Patterns, 3(10)

Bagal V et al (2022) MolGPT: molecular generation using a Transformer-Decoder model. J Chem Inf Model, (9): p. 62

Mao J et al (2023) Transformer-Based molecular generative model for antiviral drug design. Journal of chemical information and modeling

Downs GM et al (1989) Review of ring perception algorithms for chemical graphs. J Chem Inf Comput 29(3):172–187

CAS  Google Scholar 

Ucak UV et al (2022) Retrosynthetic reaction pathway prediction through neural machine translation of atomic environments. Nat Commun, 13

Sunghwan K et al (2016) PubChem substance and compound databases. Nucleic Acids Res, (D1): p. D1202–D1213

RDKit: Open-source cheminformatics. (2024); Available from: http://www.rdkit.org

Wildman SA, Crippen GM (1999) Prediction of physicochemical parameters by atomic contributions. J Chem Inform Comput Sci 39(5):868–873

CAS  Google Scholar 

Ertl PP, Schuffenhauer A (2009) Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J Cheminform 1(1):8

PubMed  PubMed Central  Google Scholar 

Bickerton GR et al Quantifying the chemical beauty of drugs. Nature Chemistry

Yuksel A SELFormer: molecular representation learning via SELFIES Language models. MLST

Krasnov L et al Transformer-based artificial neural networks for the conversion between chemical notations. Scientific Reports

Yuan H et al (2022) SeqDiffuSeq: text diffusion with Encoder-Decoder Transformers. arXiv e-prints

Polykovskiy D et al (2020) Molecular sets (MOSES): A benchmarking platform for molecular generation models. Front Pharmacol, 11

GRichard Bickerton GVP, Jérémy Besnard S, Muresan, Hopkins AL Quantifying the chemical beauty of drugs. Nat Chem 4(2): p. 9

Shultz MD Two decades under the influence of the rule of five and the changing properties of approved oral drugs: miniperspective. J Med Chem. 62(4): p. 14

Kosugi T, Ohue M Quantitative estimate of protein-protein interaction targeting drug-likeness. IEEE

Comments (0)

No login
gif