Fetching the paper…
Reading the bibliography…
Large Language Models (LLMs) have made great strides in areas such as language processing and computer vision.
Language models are few-shot learners, Adv. Condens. Matter Phys
Brown, T., Mann, B., Ryder, N. et al. (2020) · 1901
Earlier work this paper cites.
Pro_ligand: an approach to de novo molecular design. 2. design of novel molecules from molecular field analysis (mfa) models and pharmacophores, J. Med. Chem
Waszkowycz, B., Clark, D. E., Frenkel, D., Li, J., Murray, C. W., Robson, B. and Westhead, D. R. (1994) · 1994
Earlier work this paper cites.
Sprout, hippo and caesa: Tools for de novo structure generation and estimation of synthetic accessibility, Perspect. Drug Discovery Des
Gillet, V. J., Myatt, G., Zsoldos, Z. and Johnson, A. P. (1995) · 1995
Earlier work this paper cites.
Estimating the cost of new drug development: is it really $802 million?, Health Aff (Millwood)
Adams, C. P. and Brantner, V. V. (2006) · 2006
Earlier work this paper cites.
The cost of new drug discovery and development, Discov Med
Dickson, M. and Gagnon, J. P. (2009) · 2009
Earlier work this paper cites.
Ligand-based virtual screening procedure for the prediction and the identification of novel β \beta -amyloid aggregation inhibitors using kohonen maps and counterpropagation artificial neural networks, Eur. J. Med. Chem
Afantitis, A., Melagraki, G., Koutentis, P. A., Sarimveis, H. and Kollias, G. (2011) · 2011
Earlier work this paper cites.
Towards a universal smiles representation-a standard method to generate canonical smiles based on the inchi, J. Cheminformatics
O’Boyle, N. M. (2012) · 2012
Earlier work this paper cites.
Chembl web services: streamlining access to drug discovery data and utilities, Nucleic Acids Res
Davies, M., Nowotka, M., Papadatos, G., Dedman, N., Gaulton, A., Atkinson, F., Bellis, L. and Overington, J. P. (2015) · 2015
Earlier work this paper cites.
Small molecules, big targets: drug discovery faces the protein–protein interaction challenge, Nat. Rev. Drug Discov
Scott, D. E., Bayly, A. R., Abell, C. and Skidmore, J. (2016) · 2016
Earlier work this paper cites.
Neural machine translation of rare words with subword units, ACL
Sennrich, R., Haddow, B. and Birch, A. (2016) · 2016
Earlier work this paper cites.
Alpaca: Intermittent execution without checkpoints, PACMPL
Maeng, K., Colin, A. and Lucia, B. (2017) · 2017
Earlier work this paper cites.
Attention is all you need, Adv. Condens. Matter Phys
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł. and Polosukhin, I. (2017) · 2017
Earlier work this paper cites.
Application of generative autoencoder in de novo molecular design, Mol Inform
Blaschke, T., Olivecrona, M., Engkvist, O., Bajorath, J. and Chen, H. (2018) · 2018
Earlier work this paper cites.
Junction tree variational autoencoder for molecular graph generation, ICML
Jin, W., Barzilay, R. and Jaakkola, T. (2018) · 2018
Earlier work this paper cites.
Multi-objective de novo drug design with conditional graph generative model, J. Cheminformatics
Li, Y., Zhang, L. and Liu, Z. (2018) · 2018
Earlier work this paper cites.
Deep reinforcement learning for de novo drug design, Sci. Adv
Popova, M. et al. (2018) · 2018
Earlier work this paper cites.
Reinforced adversarial neural computer for de novo molecular design, J Chem Inf Model
Putin, E., Asadulaev, A., Ivanenkov, Y., Aladinskiy, V., Sanchez-Lengeling, B., Aspuru-Guzik, A. and Zhavoronkov, A. (2018) · 2018
Cited alongside, same era.
Generating focused molecule libraries for drug discovery with recurrent neural networks, ACS Cent. Sci
Segler, M. H., Kogej, T., Tyrchan, C. and Waller, M. P. (2018) · 2018
Cited alongside, same era.
Moleculenet: a benchmark for molecular machine learning, Chem. Sci
Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., Leswing, K. and Pande, V. (2018) · 2018
Cited alongside, same era.
A decade of fda-approved drugs (2010–2019): trends and future directions, J. Med. Chem
Brown, D. G. and Wobst, H. J. (2021) · 2019
Cited alongside, same era.
Chembl: towards direct deposition of bioassay data, Nucleic Acids Res
Mendez, D., Gaulton, A., Bento, A. P., Chambers, J., De Veij, M., Félix, E., Magariños, M. P., Mosquera, J. F., Mutowo, P., Nowotka, M. et al. (2019) · 2019
Cited alongside, same era.
Synthon-based ligand discovery in virtual libraries of over 11 billion compounds, Nature
Sadybekov, A. A., Sadybekov, A. V., Liu, Y., Iliopoulos-Tsoutsouvas, C., Huang, X.-P., Pickett, J., Houser, B., Patel, N., Tran, N. K., Tong, F. et al. (2022) · 2022
Later among the works it cites.
Glm-130b: An open bilingual pre-trained model, arXiv preprint arXiv:2210.02414
Zeng, A., Liu, X., Du, Z. et al. (2022) · 2022
Later among the works it cites.
Gpt-4 technical report, arXiv preprint arXiv:2303.08774
Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S. et al. (2023) · 2023
Later among the works it cites.
Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, URL https://lmsys. org/blog/2023-03-30-vicuna
Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J. E. et al. (2023) · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Hierarchical generation of molecular graphs using structural motifs, ICML
Jin, W., Barzilay, R. and Jaakkola, T. (2020) · 2020
Cited alongside, same era.
A chance-constrained generative framework for sequence optimization, ICML
Liu, X., Liu, Q., Song, S. and Peng, J. (2020) · 2020
Cited alongside, same era.
Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients, Nat Cancer
Ma, J., Fong, S. H., Luo, Y., Bakkenist, C. J., Shen, J. P., Mourragui, S., Wessels, L. F., Hafner, M., Sharan, R., Peng, J. et al. (2021) · 2021
Cited alongside, same era.
Learning to extend molecular scaffolds with structural motifs, arXiv preprint arXiv:2103.03864
Maziarz, K., Jackson-Flux, H., Cameron, P. et al. (2021) · 2021
Cited alongside, same era.
FS-mol: A few-shot learning dataset of molecules, NeurIPS
Stanley, M., Bronskill, J. F., Maziarz, K., Misztela, H., Lanini, J., Segler, M., Schneider, N. and Brockschmidt, M. (2021) · 2021
Cited alongside, same era.
The evolution of commercial drug delivery technologies, Nat Biomed Eng
Vargason, A., Anselmo, A. and Mitragotri, S. (2021) · 2021
Cited alongside, same era.
Few-shot training llms for project-specific code-summarization, Proc. IEEE/ACM Int. Conf. Autom. Softw. Eng
Ahmed, T. and Devanbu, P. (2022) · 2022
Cited alongside, same era.
Li, Y., Gao, C., Song, X., Wang, X., Xu, Y. and Han, S. (2023) · 2023
Later among the works it cites.
Modeling the expansion of virtual screening libraries, Nat Chem Biol
Lyu, J., Irwin, J. and Shoichet, B. (2023) · 2023
Later among the works it cites.
Fda approvals in 2023: biomarker-positive subsets, equipoise and verification of benefit, Nat Rev Clin Oncol
Norsworthy, K. J., Lee-Alonzo, R. J. and Pazdur, R. (2024) · 2023
Later among the works it cites.
Llama: Open and efficient foundation language models, arXiv preprint arXiv:2302.13971
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F. et al. (2023) · 2023
Later among the works it cites.
Multitask joint strategies of self-supervised representation learning on biomedical networks for drug discovery, Nat Mach Intell
Wang, X., Cheng, Y., Yang, Y. et al. (2023) · 2023
Later among the works it cites.
A survey on evaluation of large language models, ACM Trans Intell Syst Technol
Chang, Y., Wang, X., Wang, J., Wu, Y., Yang, L., Zhu, K., Chen, H., Yi, X., Wang, C., Wang, Y. et al. (2024) · 2024
Closest in time.
Small molecule approaches to targeting rna, Nat. Rev. Chem
Kovachka, S., Panosetti, M., Grimaldi, B., Azoulay, S., Di Giorgio, A. and Duca, M. (2024) · 2024
Closest in time.
Large-scale chemoproteomics expedites ligand discovery and predicts ligand behavior in cells, Science
Offensperger, F., Tin, G., Duran-Frigola, M., Hahn, E., Dobner, S., Ende, C. W. a., Strohbach, J. W., Rukavina, A., Brennsteiner, V., Ogilvie, K. et al. (2024) · 2024
Closest in time.
A small-molecule tnik inhibitor targets fibrosis in preclinical and clinical models, Nature Biotechnology
Ren, F., Aliper, A., Chen, J., Zhao, H., Rao, S., Kuppe, C., Ozerov, I. V., Zhang, M., Witte, K., Kruse, C. et al. (2024) · 2024
Closest in time.
Integrating qsar modelling and deep learning in drug discovery: the emergence of deep qsar, Nature Reviews Drug Discovery
Tropsha, A., Isayev, O., Varnek, A., Schneider, G. and Cherkasov, A. (2024) · 2024
Closest in time.
Human-level few-shot concept induction through minimax entropy learning, Sci. Adv
Zhang, C., Jia, B., Zhu, Y. and Zhu, S.-C. (2024) · 2024
Closest in time.