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1. Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical sequences

Author

Schmidinger, Niklas, Schneckenreiter, Lisa, Seidl, Philipp, Schimunek, Johannes, Hoedt, Pieter-Jan, Brandstetter, Johannes, Mayr, Andreas, Luukkonen, Sohvi, Hochreiter, Sepp, and Klambauer, Günter

Subjects
Quantitative Biology - Biomolecules, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Language models for biological and chemical sequences enable crucial applications such as drug discovery, protein engineering, and precision medicine. Currently, these language models are predominantly based on Transformer architectures. While Transformers have yielded impressive results, their quadratic runtime dependency on the sequence length complicates their use for long genomic sequences and in-context learning on proteins and chemical sequences. Recently, the recurrent xLSTM architecture has been shown to perform favorably compared to Transformers and modern state-space model (SSM) architectures in the natural language domain. Similar to SSMs, xLSTMs have a linear runtime dependency on the sequence length and allow for constant-memory decoding at inference time, which makes them prime candidates for modeling long-range dependencies in biological and chemical sequences. In this work, we tailor xLSTM towards these domains and propose a suite of architectural variants called Bio-xLSTM. Extensive experiments in three large domains, genomics, proteins, and chemistry, were performed to assess xLSTM's ability to model biological and chemical sequences. The results show that models based on Bio-xLSTM a) can serve as proficient generative models for DNA, protein, and chemical sequences, b) learn rich representations for those modalities, and c) can perform in-context learning for proteins and small molecules.

Published
2024

2. Context-enriched molecule representations improve few-shot drug discovery

Author

Schimunek, Johannes, Seidl, Philipp, Friedrich, Lukas, Kuhn, Daniel, Rippmann, Friedrich, Hochreiter, Sepp, and Klambauer, Günter

Subjects
Quantitative Biology - Biomolecules, Computer Science - Machine Learning
Abstract

A central task in computational drug discovery is to construct models from known active molecules to find further promising molecules for subsequent screening. However, typically only very few active molecules are known. Therefore, few-shot learning methods have the potential to improve the effectiveness of this critical phase of the drug discovery process. We introduce a new method for few-shot drug discovery. Its main idea is to enrich a molecule representation by knowledge about known context or reference molecules. Our novel concept for molecule representation enrichment is to associate molecules from both the support set and the query set with a large set of reference (context) molecules through a Modern Hopfield Network. Intuitively, this enrichment step is analogous to a human expert who would associate a given molecule with familiar molecules whose properties are known. The enrichment step reinforces and amplifies the covariance structure of the data, while simultaneously removing spurious correlations arising from the decoration of molecules. Our approach is compared with other few-shot methods for drug discovery on the FS-Mol benchmark dataset. On FS-Mol, our approach outperforms all compared methods and therefore sets a new state-of-the art for few-shot learning in drug discovery. An ablation study shows that the enrichment step of our method is the key to improve the predictive quality. In a domain shift experiment, we further demonstrate the robustness of our method. Code is available at https://github.com/ml-jku/MHNfs.

Published
2023

3. Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks

Author

Hofmarcher, Markus, Mayr, Andreas, Rumetshofer, Elisabeth, Ruch, Peter, Renz, Philipp, Schimunek, Johannes, Seidl, Philipp, Vall, Andreu, Widrich, Michael, Hochreiter, Sepp, and Klambauer, Günter

Subjects
Quantitative Biology - Biomolecules, Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods, Statistics - Machine Learning
Abstract

Due to the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there is an urgent need for novel therapies and drugs. We conducted a large-scale virtual screening for small molecules that are potential CoV-2 inhibitors. To this end, we utilized "ChemAI", a deep neural network trained on more than 220M data points across 3.6M molecules from three public drug-discovery databases. With ChemAI, we screened and ranked one billion molecules from the ZINC database for favourable effects against CoV-2. We then reduced the result to the 30,000 top-ranked compounds, which are readily accessible and purchasable via the ZINC database. Additionally, we screened the DrugBank using ChemAI to allow for drug repurposing, which would be a fast way towards a therapy. We provide these top-ranked compounds of ZINC and DrugBank as a library for further screening with bioassays at https://github.com/ml-jku/sars-cov-inhibitors-chemai., Comment: Additional results added. Various corrections to formulations and typos

Published
2020

4. A community effort in SARS‐CoV‐2 drug discovery

Author

Schimunek, Johannes, primary, Seidl, Philipp, additional, Elez, Katarina, additional, Hempel, Tim, additional, Le, Tuan, additional, Noé, Frank, additional, Olsson, Simon, additional, Raich, Lluís, additional, Winter, Robin, additional, Gokcan, Hatice, additional, Gusev, Filipp, additional, Gutkin, Evgeny M., additional, Isayev, Olexandr, additional, Kurnikova, Maria G., additional, Narangoda, Chamali H., additional, Zubatyuk, Roman, additional, Bosko, Ivan P., additional, Furs, Konstantin V., additional, Karpenko, Anna D., additional, Kornoushenko, Yury V., additional, Shuldau, Mikita, additional, Yushkevich, Artsemi, additional, Benabderrahmane, Mohammed, additional, Bousquet-Melou, Patrick, additional, Bureau, Ronan, additional, Charton, Beatrice, additional, Cirou, Bertrand, additional, Gil, Gérard, additional, Allen, William J., additional, Sirimulla, Suman, additional, Watowich, Stanley, additional, Antonopoulos, Nick, additional, Epitropakis, Nikolaos, additional, Krasoulis, Agamemnon, additional, Pitsikalis, Vassilis, additional, Theodorakis, Stavros, additional, Kozlovskii, Igor, additional, Maliutin, Anton, additional, Medvedev, Alexander, additional, Popov, Petr, additional, Zaretckii, Mark, additional, Eghbal-zadeh, Hamid, additional, Halmich, Christina, additional, Hochreiter, Sepp, additional, Mayr, Andreas, additional, Ruch, Peter, additional, Widrich, Michael, additional, Berenger, Francois, additional, Kumar, Ashutosh, additional, Yamanishi, Yoshihiro, additional, Zhang, Kam, additional, Bengio, Emmanuel, additional, Bengio, Yoshua, additional, Jain, Moksh, additional, Korablyov, Maksym, additional, Liu, Cheng-Hao, additional, Gilles, Marcous, additional, Glaab, Enrico, additional, Barnsley, Kelly, additional, Iyengar, Suhasini M., additional, Ondrechen, Mary Jo, additional, Haupt, V. Joachim, additional, Kaiser, Florian, additional, Schroeder, Michael, additional, Pugliese, Luisa, additional, Albani, Simone, additional, Athanasiou, Christina, additional, Beccari, Andrea, additional, Carloni, Paolo, additional, D'Arrigo, Giulia, additional, Gianquinto, Eleonora, additional, Goßen, Jonas, additional, Hanke, Anton, additional, Joseph, Benjamin P., additional, Kokh, Daria B., additional, Kovachka, Sandra, additional, Manelfi, Candida, additional, Mukherjee, Goutam, additional, Muñiz-Chicharro, Abraham, additional, Musiani, Francesco, additional, Nunes-Alves, Ariane, additional, Paiardi, Giulia, additional, Rossetti, Giulia, additional, Sadiq, S. Kashif, additional, Spyrakis, Francesca, additional, Talarico, Carmine, additional, Tsengenes, Alexandros, additional, Wade, Rebecca, additional, Copeland, Conner, additional, Gaiser, Jeremiah, additional, Olson, Daniel R., additional, Roy, Amitava, additional, Venkatraman, Vishwesh, additional, Wheeler, Travis J., additional, Arthanari, Haribabu, additional, Blaschitz, Klara, additional, Cespugli, Marco, additional, Durmaz, Vedat, additional, Fackeldey, Konstantin, additional, Fischer, Patrick D., additional, Gorgulla, Christoph, additional, Gruber, Christian, additional, Gruber, Karl, additional, Hetmann, Michael, additional, Kinney, Jamie E., additional, Das, Krishna M. Padmanabha, additional, Pandita, Shreya, additional, Singh, Amit, additional, Steinkellner, Georg, additional, Tesseyre, Guilhem, additional, Wagner, Gerhard, additional, Wang, Zi-Fu, additional, Yust, Ryan J., additional, Druzhilovskiy, Dmitry S., additional, Filimonov, Dmitry, additional, Pogodin, Pavel V., additional, Poroikov, Vladimir, additional, Rudik, Anastassia V., additional, Stolbov, Leonid A., additional, Veselovsky, Alexander V., additional, De Rosa, Maria, additional, Simone, Giada De, additional, Gulotta, Maria R., additional, Lombino, Jessica, additional, Mekni, Nedra, additional, Perricone, Ugo, additional, Casini, Arturo, additional, Embree, Amanda, additional, Gordon, D. Benjamin, additional, Lei, David, additional, Pratt, Katelin, additional, Voigt, Christopher A., additional, Chen, Kuang-Yu, additional, Jacob, Yves, additional, Krischuns, Tim, additional, Lafaye, Pierre, additional, Zettor, Agnès, additional, Rodríguez, M. Luis, additional, White, Kris M., additional, Fearon, Daren, additional, von Delft, Frank, additional, Walsh, Martin A., additional, Horvath, Dragos, additional, Brooks, Charles L., additional, Falsafi, Babak, additional, Ford, Bryan, additional, García-Sastre, Adolfo, additional, Lee, Sang Yup, additional, Naffakh, Nadia, additional, Varnek, Alexandre, additional, Klambauer, Guenter, additional, and Hermans, Thomas M., additional

Published
2023
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5. A community effort in SARS‐CoV‐2 drug discovery.

Author

Schimunek, Johannes, Seidl, Philipp, Elez, Katarina, Hempel, Tim, Le, Tuan, Noé, Frank, Olsson, Simon, Raich, Lluís, Winter, Robin, Gokcan, Hatice, Gusev, Filipp, Gutkin, Evgeny M., Isayev, Olexandr, Kurnikova, Maria G., Narangoda, Chamali H., Zubatyuk, Roman, Bosko, Ivan P., Furs, Konstantin V., Karpenko, Anna D., and Kornoushenko, Yury V.

Subjects
DRUG discovery, SARS-CoV-2, CONTRACT research organizations, OPEN scholarship, MIDDLE-income countries
Abstract

The COVID‐19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small‐molecule drugs that are widely available, including in low‐ and middle‐income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the "Billion molecules against COVID‐19 challenge", to identify small‐molecule inhibitors against SARS‐CoV‐2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 molecules, which were subsequently ranked to find 'consensus compounds'. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for biological activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (only the Nsp12 domain), and (alpha) spike protein S. Overall, 27 compounds with weak inhibition/binding were experimentally identified by binding‐, cleavage‐, and/or viral suppression assays and are presented here. Open science approaches such as the one presented here contribute to the knowledge base of future drug discovery efforts in finding better SARS‐CoV‐2 treatments. [ABSTRACT FROM AUTHOR]

Published
2024
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6. A community effort to discover small molecule SARS-CoV-2 inhibitors

Author

Schimunek, Johannes, primary, Seidl, Philipp, additional, Elez, Katarina, additional, Hempel, Tim, additional, Le, Tuan, additional, Noé, Frank, additional, Olsson, Simon, additional, Raich, Lluís, additional, Winter, Robin, additional, Gokcan, Hatice, additional, Gusev, Filipp, additional, Gutkin, Evgeny M., additional, Isayev, Olexandr, additional, Kurnikova, Maria G., additional, Narangoda, Chamali H., additional, Zubatyuk, Roman, additional, Bosko, Ivan P., additional, Furs, Konstantin V., additional, Karpenko, Anna D., additional, Kornoushenko, Yury V., additional, Shuldau, Mikita, additional, Yushkevich, Artsemi, additional, Benabderrahmane, Mohammed B., additional, Bousquet-Melou, Patrick, additional, Bureau, Ronan, additional, Charton, Beatrice, additional, Cirou, Bertrand C., additional, Gil, Gérard, additional, Allen, William J., additional, Sirimulla, Suman, additional, Watowich, Stanley, additional, Antonopoulos, Nick A., additional, Epitropakis, Nikolaos E., additional, Krasoulis, Agamemnon K., additional, Pitsikalis, Vassilis P., additional, Theodorakis, Stavros T., additional, Kozlovskii, Igor, additional, Maliutin, Anton, additional, Medvedev, Alexander, additional, Popov, Petr, additional, Zaretckii, Mark, additional, Eghbal-zadeh, Hamid, additional, Halmich, Christina, additional, Hochreiter, Sepp, additional, Mayr, Andreas, additional, Ruch, Peter, additional, Widrich, Michael, additional, Berenger, Francois, additional, Kumar, Ashutosh, additional, Yamanishi, Yoshihiro, additional, Zhang, Kam Y.J., additional, Bengio, Emmanuel, additional, Bengio, Yoshua, additional, Jain, Moksh J., additional, Korablyov, Maksym, additional, Liu, Cheng-Hao, additional, Marcou, Gilles, additional, Glaab, Enrico, additional, Barnsley, Kelly, additional, Iyengar, Suhasini M., additional, Ondrechen, Mary Jo, additional, Haupt, V. Joachim, additional, Kaiser, Florian, additional, Schroeder, Michael, additional, Pugliese, Luisa, additional, Albani, Simone, additional, Athanasiou, Christina, additional, Beccari, Andrea, additional, Carloni, Paolo, additional, D'Arrigo, Giulia, additional, Gianquinto, Eleonora, additional, Goßen, Jonas, additional, Hanke, Anton, additional, Joseph, Benjamin P., additional, Kokh, Daria B., additional, Kovachka, Sandra, additional, Manelfi, Candida, additional, Mukherjee, Goutam, additional, Muñiz-Chicharro, Abraham, additional, Musiani, Francesco, additional, Nunes-Alves, Ariane, additional, Paiardi, Giulia, additional, Rossetti, Giulia, additional, Sadiq, S. Kashif, additional, Spyrakis, Francesca, additional, Talarico, Carmine, additional, Tsengenes, Alexandros, additional, Wade, Rebecca C., additional, Copeland, Conner, additional, Gaiser, Jeremiah, additional, Olson, Daniel R., additional, Roy, Amitava, additional, Venkatraman, Vishwesh, additional, Wheeler, Travis J., additional, Arthanari, Haribabu, additional, Blaschitz, Klara, additional, Cespugli, Marco, additional, Durmaz, Vedat, additional, Fackeldey, Konstantin, additional, Fischer, Patrick D., additional, Gorgulla, Christoph, additional, Gruber, Christian, additional, Gruber, Karl, additional, Hetmann, Michael, additional, Kinney, Jamie E., additional, Padmanabha Das, Krishna M., additional, Pandita, Shreya, additional, Singh, Amit, additional, Steinkellner, Georg, additional, Tesseyre, Guilhem, additional, Wagner, Gerhard, additional, Wang, Zi-Fu, additional, Yust, Ryan J., additional, Druzhilovskiy, Dmitry S., additional, Filimonov, Dmitry A., additional, Pogodin, Pavel V., additional, Poroikov, Vladimir, additional, Rudik, Anastassia V., additional, Stolbov, Leonid A., additional, Veselovsky, Alexander V., additional, De Rosa, Maria, additional, De Simone, Giada, additional, Gulotta, Maria R., additional, Lombino, Jessica, additional, Mekni, Nedra, additional, Perricone, Ugo, additional, Casini, Arturo, additional, Embree, Amanda, additional, Gordon, D. Benjamin, additional, Lei, David, additional, Pratt, Katelin, additional, Voigt, Christopher A., additional, Chen, Kuang-Yu, additional, Jacob, Yves, additional, Krischuns, Tim, additional, Lafaye, Pierre, additional, Zettor, Agnès, additional, Rodríguez, M. Luis, additional, White, Kris M., additional, Fearon, Daren, additional, Von Delft, Frank, additional, Walsh, Martin A., additional, Horvath, Dragos, additional, Brooks III, Charles L., additional, Falsafi, Babak, additional, Ford, Bryan, additional, García-Sastre, Adolfo, additional, Lee, Sang Yup, additional, Naffakh, Nadia, additional, Varnek, Alexandre, additional, Klambauer, Günter, additional, and Hermans, Thomas M., additional

Published
2023
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