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The integration of Large Language Models (LLMs) into the drug discovery and development field marks a significant paradigm shift, offering novel methodologies for understanding disease mechanisms, facilitating drug discovery, and optimizing clinical trial processes.
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The path to oncology drug target validation: an industry perspective
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Shape-seq 2.0: Systematic optimization and extension of high-throughput chemical probing of rna secondary structure with next generation sequencing
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Improvements to bm25 and language models examined
Trotman, A., Puurula, A., and Burgess, B · 2014
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Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer
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The support of human genetic evidence for approved drug indications
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Rational design of viscosity reducing mutants of a monoclonal antibody: hydrophobic versus electrostatic inter-molecular interactions
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Human disease modeling reveals integrated transcriptional and epigenetic mechanisms of notch1 haploinsufficiency
Theodoris, C. V., Li, M., White, M. P., Liu, L., He, D., Pollard, K. S., Bruneau, B. G., and Srivastava, D · 2015
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Developing predictive assays: the phenotypic screening “rule of 3”
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Xgboost: A scalable tree boosting system
Chen, T. and Guestrin, C · 2016
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Retain: An interpretable predictive model for healthcare using reverse time attention mechanism
Choi, E., Bahadori, M. T., Sun, J., Kulas, J., Schuetz, A., and Stewart, W · 2016
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Dms-mapseq for genome-wide or targeted rna structure probing in vivo
Zubradt, M., Gupta, P., Persad, S., Lambowitz, A. M., Weissman, J. S., and Rouskin, S · 2016
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The interplay of epigenetic marks during stem cell differentiation and development
Atlasi, Y. and Stunnenberg, H. G · 2017
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Strategies and challenges for the next generation of antibody–drug conjugates
Beck, A., Goetsch, L., Dumontet, C., and Corvaïa, N · 2017
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Objective-reinforced generative adversarial networks (organ) for sequence generation models
Guimaraes, G. L., Sanchez-Lengeling, B., Outeiral, C., Farias, P. L. C., and Aspuru-Guzik, A · 2017
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Crispr/cas9 mutagenesis invalidates a putative cancer dependency targeted in on-going clinical trials
Lin, A., Giuliano, C. J., Sayles, N. M., and Sheltzer, J. M · 2017
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Dipole: Diagnosis prediction in healthcare via attention-based bidirectional recurrent neural networks
Ma, F., Chitta, R., Zhou, J., You, Q., Sun, T., and Gao, J · 2017
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Automating biomedical evidence synthesis: Robotreviewer
Marshall, I. J., Kuiper, J., Banner, E., and Wallace, B. C · 2017
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Association analysis identifies 65 new breast cancer risk loci
Michailidou, K., Lindström, S., Dennis, J., Beesley, J., Hui, S., Kar, S., Lemaçon, A., Soucy, P., Glubb, D., Rostamianfar, A., et al · 2017
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Association analyses based on false discovery rate implicate new loci for coronary artery disease
Nelson, C. P., Goel, A., Butterworth, A. S., Kanoni, S., Webb, T. R., Marouli, E., Zeng, L., Ntalla, I., Lai, F. Y., Hopewell, J. C., et al · 2017
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Molecular de-novo design through deep reinforcement learning
Olivecrona, M., Blaschke, T., Engkvist, O., and Chen, H · 2017
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Towards automated icd coding using deep learning
Shi, H., Xie, P., Hu, Z., Zhang, M., and Xing, E. P · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Musitedeep: a deep-learning framework for general and kinase-specific phosphorylation site prediction
Wang, D., Zeng, S., Xu, C., Qiu, W., Liang, Y., Joshi, T., and Xu, D · 2017
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Genetic-driven druggable target identification and validation
Floris, M., Olla, S., Schlessinger, D., and Cucca, F · 2018
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Tienet: Text-image embedding network for common thorax disease classification and reporting in chest x-rays
Wang, X., Peng, Y., Lu, L., Lu, Z., and Summers, R. M · 2018
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A neural architecture for automated icd coding
Xie, P. and Xing, E · 2018
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Raim: Recurrent attentive and intensive model of multimodal patient monitoring data
Xu, Y., Biswal, S., Deshpande, S. R., Maher, K. O., and Sun, J · 2018
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Genome-wide analyses using uk biobank data provide insights into the genetic architecture of osteoarthritis
Zengini, E., Hatzikotoulas, K., Tachmazidou, I., Steinberg, J., Hartwig, F. P., Southam, L., Hackinger, S., Boer, C. G., Styrkarsdottir, U., Gilly, A., et al · 2018
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Publicly available clinical bert embeddings
Alsentzer, E., Murphy, J. R., Boag, W., Weng, W.-H., Jin, D., Naumann, T., and McDermott, M · 2019
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Guacamol: benchmarking models for de novo molecular design
Brown, N., Fiscato, M., Segler, M. H., and Vaucher, A. C · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
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Deepphos: prediction of protein phosphorylation sites with deep learning
Luo, F., Wang, M., Liu, Y., Zhao, X.-M., and Li, A · 2019
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Epidrugs: targeting epigenetic marks in cancer treatment
Miranda Furtado, C. L., Dos Santos Luciano, M. C., Silva Santos, R. D., Furtado, G. P., Moraes, M. O., and Pessoa, C · 2019
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Evaluating protein transfer learning with tape
Rao, R., Bhattacharya, N., Thomas, N., Duan, Y., Chen, P., Canny, J., Abbeel, P., and Song, Y · 2019
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Five computational developability guidelines for therapeutic antibody profiling
Raybould, M. I., Marks, C., Krawczyk, K., Taddese, B., Nowak, J., Lewis, A. P., Bujotzek, A., Shi, J., and Deane, C. M · 2019
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Molecular transformer: a model for uncertainty-calibrated chemical reaction prediction
Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C. A., Bekas, C., and Lee, A. A · 2019
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From target to drug: generative modeling for the multimodal structure-based ligand design
Skalic, M., Sabbadin, D., Sattarov, B., Sciabola, S., and De Fabritiis, G · 2019
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Deep learning enables rapid identification of potent ddr1 kinase inhibitors
Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., Terentiev, V. A., Polykovskiy, D. A., Kuznetsov, M. D., Asadulaev, A., et al · 2019
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An overview of drug discovery and development
Berdigaliyev, N. and Aljofan, M · 2020
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Reinvent 2.0: an ai tool for de novo drug design
Blaschke, T., Arús-Pous, J., Chen, H., Margreitter, C., Tyrchan, C., Engkvist, O., Papadopoulos, K., and Patronov, A · 2020
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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Using metadynamics to explore complex free-energy landscapes
Bussi, G. and Laio, A · 2020
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Generating medical reports from patient-doctor conversations using sequence-to-sequence models
Enarvi, S., Amoia, M., Del-Agua Teba, M., Delaney, B., Diehl, F., Hahn, S., Harris, K., McGrath, L., Pan, Y., Pinto, J., Rubini, L., Ruiz, M., Singh, G., Stemmer, F., Sun, W., Vozila, P., Lin, T., and Ramamurthy, R · 2020
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Stagenet: Stage-aware neural networks for health risk prediction
Gao, J., Xiao, C., Wang, Y., Tang, W., Glass, L. M., and Sun, J · 2020
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Clinicalbert: Modeling clinical notes and predicting hospital readmission
Huang, K., Altosaar, J., and Ranganath, R · 2020
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Biobert: a pre-trained biomedical language representation model for biomedical text mining
Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., and Kang, J · 2020
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BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., and Zettlemoyer, L · 2020
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Progen: Language modeling for protein generation
Madani, A., McCann, B., Naik, N., Keskar, N. S., Anand, N., Eguchi, R. R., Huang, P.-S., and Socher, R · 2020
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A universal system for digitization and automatic execution of the chemical synthesis literature
Mehr, S. H. M., Craven, M., Leonov, A. I., Keenan, G., and Cronin, L · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J · 2020
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Transformer protein language models are unsupervised structure learners
Rao, R., Meier, J., Sercu, T., Ovchinnikov, S., and Rives, A · 2020
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Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy
Schwaller, P., Petraglia, R., Zullo, V., Nair, V. H., Haeuselmann, R. A., Pisoni, R., Bekas, C., Iuliano, A., and Laino, T · 2020
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Methods for numeracy-preserving word embeddings
Sundararaman, D., Si, S., Subramanian, V., Wang, G., Hazarika, D., and Carin, L · 2020
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Deepenroll: patient-trial matching with deep embedding and entailment prediction
Zhang, X., Xiao, C., Glass, L. M., and Sun, J · 2020
Cited alongside, same era.
Accurate prediction of protein structures and interactions using a three-track neural network
Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., et al · 2021
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Learning the protein language: Evolution, structure, and function
Bepler, T. and Berger, B · 2021
Cited alongside, same era.
Diffdock: Diffusion steps, twists, and turns for molecular docking
Corso, G., Stärk, H., Jing, B., Barzilay, R., and Jaakkola, T. S · 2023
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scgpt: towards building a foundation model for single-cell multi-omics using generative ai
Cui, H., Wang, C., Maan, H., Pang, K., Luo, F., and Wang, B · 2023
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The nucleotide transformer: Building and evaluating robust foundation models for human genomics
Dalla-Torre, H., Gonzalez, L., Mendoza-Revilla, J., Carranza, N. L., Grzywaczewski, A. H., Oteri, F., Dallago, C., Trop, E., de Almeida, B. P., Sirelkhatim, H., et al · 2023
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Faith and fate: Limits of transformers on compositionality
Dziri, N., Lu, X., Sclar, M., Li, X. L., Jian, L., Lin, B. Y., West, P., Bhagavatula, C., Bras, R. L., Hwang, J. D., et al · 2023
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Deepprosite: Structure-aware protein binding site prediction using esmfold and pretrained language model
Fang, Y., Jiang, Y., Wei, L., Ma, Q., Ren, Z., Yuan, Q., and Wei, D.-Q · 2023
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Machine learning in qm/mm molecular dynamics simulations of condensed-phase systems
Böselt, L., Thürlemann, M., and Riniker, S · 2021
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Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations
Das, P., Sercu, T., Wadhawan, K., Padhi, I., Gehrmann, S., Cipcigan, F., Chenthamarakshan, V., Strobelt, H., dos Santos, C., Chen, P.-Y., Yang, Y. Y., Tan, J. P. K., Hedrick, J., Crain, J., and Mojsilovic, A · 2021
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Prottrans: Toward understanding the language of life through self-supervised learning
Elnaggar, A., Heinzinger, M., Dallago, C., Rehawi, G., Wang, Y., Jones, L., Gibbs, T., Feher, T., Angerer, C., Steinegger, M., et al · 2021
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Improving target assessment in biomedical research: the got-it recommendations
Emmerich, C. H., Gamboa, L. M., Hofmann, M. C., Bonin-Andresen, M., Arbach, O., Schendel, P., Gerlach, B., Hempel, K., Bespalov, A., Dirnagl, U., et al · 2021
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Mermaid: an open source automated hit-to-lead method based on deep reinforcement learning
Erikawa, D., Yasuo, N., and Sekijima, M · 2021
Cited alongside, same era.
Protein complex prediction with alphafold-multimer
Evans, R., O’Neill, M., Pritzel, A., Antropova, N., Senior, A., Green, T., Žídek, A., Bates, R., Blackwell, S., Yim, J., et al · 2021
Cited alongside, same era.
Domain-specific language model pretraining for biomedical natural language processing
Gu, Y., Tinn, R., Cheng, H., Lucas, M., Usuyama, N., Liu, X., Naumann, T., Gao, J., and Poon, H · 2021
Cited alongside, same era.
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Mathematical capabilities of chatgpt
Frieder, S., Pinchetti, L., Griffiths, R.-R., Salvatori, T., Lukasiewicz, T., Petersen, P. C., Chevalier, A., and Berner, J · 2023
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xval: A continuous number encoding for large language models
Golkar, S., Pettee, M., Eickenberg, M., Bietti, A., Cranmer, M., Krawezik, G., Lanusse, F., McCabe, M., Ohana, R., Parker, L., et al · 2023
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Studying large language model generalization with influence functions
Grosse, R., Bae, J., Anil, C., Elhage, N., Tamkin, A., Tajdini, A., Steiner, B., Li, D., Durmus, E., Perez, E., et al · 2023
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Large language models are zero-shot time series forecasters
Gruver, N., Finzi, M., Qiu, S., and Wilson, A. G · 2023
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Proteinchat: Towards achieving chatgpt-like functionalities on protein 3d structures
Guo, H., Huo, M., Zhang, R., and Xie, P · 2023
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Improving patient pre-screening for clinical trials: Assisting physicians with large language models
Hamer, D. M. d., Schoor, P., Polak, T. B., and Kapitan, D · 2023
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Efficient evolution of human antibodies from general protein language models
Hie, B. L., Shanker, V. R., Xu, D., Bruun, T. U., Weidenbacher, P. A., Tang, S., Wu, W., Pak, J. E., and Kim, P. S · 2023
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Reference-free and cost-effective automated cell type annotation with gpt-4 in single-cell rna-seq analysis
Hou, W. and Ji, Z · 2023
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Inagaki, T., Kato, A., Takahashi, K., Ozaki, H., and Kanda, G. N · 2023
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Chemistry42: an ai-driven platform for molecular design and optimization
Ivanenkov, Y. A., Polykovskiy, D., Bezrukov, D., Zagribelnyy, B., Aladinskiy, V., Kamya, P., Aliper, A., Ren, F., and Zhavoronkov, A · 2023
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Chatgpt makes medicine easy to swallow: an exploratory case study on simplified radiology reports
Jeblick, K., Schachtner, B., Dexl, J., Mittermeier, A., Stüber, A. T., Topalis, J., Weber, T., Wesp, P., Sabel, B. O., Ricke, J., et al · 2023
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Survey of hallucination in natural language generation
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., and Fung, P · 2023
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Health system-scale language models are all-purpose prediction engines
Jiang, L. Y., Liu, X. C., Nejatian, N. P., Nasir-Moin, M., Wang, D., Abidin, A., Eaton, K., Riina, H. A., Laufer, I., Punjabi, P., et al · 2023
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Diffdock-PP: Rigid protein-protein docking with diffusion models
Ketata, M. A., Laue, C., Mammadov, R., Stark, H., Wu, M., Corso, G., Marquet, C., Barzilay, R., and Jaakkola, T. S · 2023
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Performance of chatgpt on usmle: potential for ai-assisted medical education using large language models
Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., et al · 2023
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Evolutionary-scale prediction of atomic-level protein structure with a language model
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al · 2023
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Reinvent4: Modern ai–driven generative molecule design
Loeffler, H., He, J., Tibo, A., Janet, J. P., Voronov, A., Mervin, L., and Engkvist, O · 2023
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Large language models generate functional protein sequences across diverse families
Madani, A., Krause, B., Greene, E. R., Subramanian, S., Mohr, B. P., Holton, J. M., Olmos Jr, J. L., Xiong, C., Sun, Z. Z., Socher, R., et al · 2023
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Deep learning for flexible and site-specific protein docking and design
McPartlon, M. and Xu, J · 2023
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Leveraging molecular structure and bioactivity with chemical language models for de novo drug design
Moret, M., Pachon Angona, I., Cotos, L., Yan, S., Atz, K., Brunner, C., Baumgartner, M., Grisoni, F., and Schneider, G · 2023
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A publication-wide association study (pwas), historical language models to prioritise novel therapeutic drug targets
Narganes-Carlón, D., Crowther, D. J., and Pearson, E. R · 2023
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Scalable extraction of training data from (production) language models
Nasr, M., Carlini, N., Hayase, J., Jagielski, M., Cooper, A. F., Ippolito, D., Choquette-Choo, C. A., Wallace, E., Tramèr, F., and Lee, K · 2023
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Progen2: exploring the boundaries of protein language models
Nijkamp, E., Ruffolo, J. A., Weinstein, E. N., Naik, N., and Madani, A · 2023
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OpenAI · 2023
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Unifying large language models and knowledge graphs: A roadmap
Pan, S., Luo, L., Wang, Y., Chen, C., Wang, J., and Wu, X · 2023
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Automated extraction of molecular interactions and pathway knowledge using large language model, galactica: Opportunities and challenges
Park, G., Yoon, B.-J., Luo, X., Lpez-Marrero, V., Johnstone, P., Yoo, S., and Alexander, F · 2023
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Chatgpt: the future of discharge summaries?
Patel, S. B. and Lam, K · 2023
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Penedo, G., Malartic, Q., Hesslow, D., Cojocaru, R., Cappelli, A., Alobeidli, H., Pannier, B., Almazrouei, E., and Launay, J · 2023
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Ai-powered therapeutic target discovery
Pun, F. W., Ozerov, I. V., and Zhavoronkov, A · 2023
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Alphafold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel cdk20 small molecule inhibitor
Ren, F., Ding, X., Zheng, M., Korzinkin, M., Cai, X., Zhu, W., Mantsyzov, A., Aliper, A., Aladinskiy, V., Cao, Z., et al · 2023
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Code llama: Open foundation models for code
Roziere, B., Gehring, J., Gloeckle, F., Sootla, S., Gat, I., Tan, X. E., Adi, Y., Liu, J., Remez, T., Rapin, J., et al · 2023
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Drug discovery companies are customizing chatgpt: here’s how
Savage, N · 2023
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Contrastive learning in protein language space predicts interactions between drugs and protein targets
Singh, R., Sledzieski, S., Bryson, B., Cowen, L., and Berger, B · 2023
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Large language models encode clinical knowledge
Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al · 2023
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Testolin, A · 2023
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Transfer learning enables predictions in network biology
Theodoris, C. V., Xiao, L., Chopra, A., Chaffin, M. D., Al Sayed, Z. R., Hill, M. C., Mantineo, H., Brydon, E. M., Zeng, Z., Liu, X. S., et al · 2023
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Framm: Fair ranking with missing modalities for clinical trial site selection
Theodorou, B., Glass, L., Xiao, C., and Sun, J · 2023
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Large language models in medicine
Thirunavukarasu, A. J., Ting, D. S. J., Elangovan, K., Gutierrez, L., Tan, T. F., and Ting, D. S. W · 2023
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Harnessing large language models (llms) for candidate gene prioritization and selection
Toufiq, M., Rinchai, D., Bettacchioli, E., Kabeer, B. S. A., Khan, T., Subba, B., White, O., Yurieva, M., George, J., Jourde-Chiche, N., et al · 2023
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The impact of alphafold protein structure database on the fields of life sciences
Varadi, M. and Velankar, S · 2023
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De novo design of protein structure and function with rfdiffusion
Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., et al · 2023
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Six ways large language models are changing healthcare
Webster, P · 2023
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Clinidigest: a case study in large language model based large-scale summarization of clinical trial descriptions
White, R., Peng, T., Sripitak, P., Rosenberg Johansen, A., and Snyder, M · 2023
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A survey on multimodal large language models
Yin, S., Fu, C., Zhao, S., Li, K., Sun, X., Xu, T., and Chen, E · 2023
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Large language models for chemistry robotics
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