Search Results - "Nigam, AkshatKumar"

Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation by Krenn, Mario, Häse, Florian, Nigam, AkshatKumar, Friederich, Pascal, Aspuru-Guzik, Alan

“…The discovery of novel materials and functional molecules can help to solve some of society's most urgent challenges, ranging from efficient energy harvesting…”
Get full text

Parallel tempered genetic algorithm guided by deep neural networks for inverse molecular design by Nigam, AkshatKumar, Pollice, Robert, Aspuru-Guzik, Alán

“…Inverse molecular design involves algorithms that sample molecules with specific target properties from a multitude of candidates and can be posed as an…”
Get full text

Beyond generative models: superfast traversal, optimization, novelty, exploration and discovery (STONED) algorithm for molecules using SELFIES by Nigam, AkshatKumar, Pollice, Robert, Krenn, Mario, Gomes, Gabriel dos Passos, Aspuru-Guzik, Alán

“…Inverse design allows the generation of molecules with desirable physical quantities using property optimization. Deep generative models have recently been…”
Get full text

Artificial design of organic emitters a genetic algorithm enhanced by a deep neural network by Nigam, AkshatKumar, Pollice, Robert, Friederich, Pascal, Aspuru-Guzik, Aln

“…The design of molecules requires multi-objective optimizations in high-dimensional chemical space with often conflicting target properties. To navigate this…”
Get full text

Data-Driven Strategies for Accelerated Materials Design by Pollice, Robert, dos Passos Gomes, Gabriel, Aldeghi, Matteo, Hickman, Riley J, Krenn, Mario, Lavigne, Cyrille, Lindner-D’Addario, Michael, Nigam, AkshatKumar, Ser, Cher Tian, Yao, Zhenpeng, Aspuru-Guzik, Alán

“…Conspectus The ongoing revolution of the natural sciences by the advent of machine learning and artificial intelligence sparked significant interest in the…”
Get full text

Artificial design of organic emitters via a genetic algorithm enhanced by a deep neural network by Nigam, AkshatKumar, Pollice, Robert, Friederich, Pascal, Aspuru-Guzik, Alán

“…The design of molecules requires multi-objective optimizations in high-dimensional chemical space with often conflicting target properties. To navigate this…”
Get full text

A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis by Gensch, Tobias, dos Passos Gomes, Gabriel, Friederich, Pascal, Peters, Ellyn, Gaudin, Théophile, Pollice, Robert, Jorner, Kjell, Nigam, AkshatKumar, Lindner-D’Addario, Michael, Sigman, Matthew S, Aspuru-Guzik, Alán

“…The design of molecular catalysts typically involves reconciling multiple conflicting property requirements, largely relying on human intuition and local…”
Get full text

SELFIES and the future of molecular string representations by Krenn, Mario, Ai, Qianxiang, Barthel, Senja, Carson, Nessa, Frei, Angelo, Frey, Nathan C., Friederich, Pascal, Gaudin, Théophile, Gayle, Alberto Alexander, Jablonka, Kevin Maik, Lameiro, Rafael F., Lemm, Dominik, Lo, Alston, Moosavi, Seyed Mohamad, Nápoles-Duarte, José Manuel, Nigam, AkshatKumar, Pollice, Robert, Rajan, Kohulan, Schatzschneider, Ulrich, Schwaller, Philippe, Skreta, Marta, Smit, Berend, Strieth-Kalthoff, Felix, Sun, Chong, Tom, Gary, Falk von Rudorff, Guido, Wang, Andrew, White, Andrew D., Young, Adamo, Yu, Rose, Aspuru-Guzik, Alán

“…Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials…”
Get full text

On scientific understanding with artificial intelligence by Krenn, Mario, Pollice, Robert, Guo, Si Yue, Aldeghi, Matteo, Cervera-Lierta, Alba, Friederich, Pascal, dos Passos Gomes, Gabriel, Häse, Florian, Jinich, Adrian, Nigam, AkshatKumar, Yao, Zhenpeng, Aspuru-Guzik, Alán

“…An oracle that correctly predicts the outcome of every particle physics experiment, the products of every possible chemical reaction or the function of every…”
Get full text

Assigning confidence to molecular property prediction by Nigam, AkshatKumar, Pollice, Robert, Hurley, Matthew F D, Hickman, Riley J, Aldeghi, Matteo, Yoshikawa, Naruki, Chithrananda, Seyone, Voelz, Vincent A, Aspuru-Guzik, Alán

“…: Computational modeling has rapidly advanced over the last decades. Recently, machine learning has emerged as a powerful and cost-effective strategy to learn…”
Get more information

Curiosity in exploring chemical spaces: intrinsic rewards for molecular reinforcement learning by Thiede, Luca A, Krenn, Mario, Nigam, AkshatKumar, Aspuru-Guzik, Alán

“…Abstract Computer aided design of molecules has the potential to disrupt the field of drug and material discovery. Machine learning and deep learning in…”
Get full text

Recent advances in the self-referencing embedded strings (SELFIES) library by Lo, Alston, Pollice, Robert, Nigam, AkshatKumar, White, Andrew D, Krenn, Mario, Aspuru-Guzik, Alán

“…String-based molecular representations play a crucial role in cheminformatics applications, and with the growing success of deep learning in chemistry, have…”
Get full text

Application of established computational techniques to identify potential SARS-CoV-2 Nsp14-MTase inhibitors in low data regimes by Nigam, AkshatKumar, Hurley, Matthew F. D, Li, Fengling, Konko ová, Eva, Klíma, Martin, Tryl ová, Jana, Pollice, Robert, Çinaro lu, Sleyman Selim, Levin-Konigsberg, Roni, Handjaya, Jasemine, Schapira, Matthieu, Chau, Irene, Perveen, Sumera, Ng, Ho-Leung, Kaniskan, H. Ümit, Han, Yulin, Singh, Sukrit, Gorgulla, Christoph, Kundaje, Anshul, Jin, Jian, Voelz, Vincent A, Weber, Jan, Nencka, Radim, Boura, Evzen, Vedadi, Masoud, Aspuru-Guzik, Alán

“…The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has led to significant global morbidity and mortality. A crucial viral protein, the non-structural…”
Get full text

JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design by Nigam, AkshatKumar, Pollice, Robert, Aspuru-Guzik, Alan

“…Inverse molecular design, i.e., designing molecules with specific target properties, can be posed as an optimization problem. High-dimensional optimization…”
Get full text

Recent advances in the Self-Referencing Embedding Strings (SELFIES) library by Lo, Alston, Pollice, Robert, Nigam, AkshatKumar, White, Andrew D, Krenn, Mario, Aspuru-Guzik, Alán

“…Digital Discovery 2, 897 (2023) String-based molecular representations play a crucial role in cheminformatics applications, and with the growing success of…”
Get full text

Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning by Thiede, Luca A, Krenn, Mario, Nigam, AkshatKumar, Aspuru-Guzik, Alan

“…Machine Learning: Science and Technology 3, 035008 (2022) Computer-aided design of molecules has the potential to disrupt the field of drug and material…”
Get full text

Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design by Nigam, AkshatKumar, Pollice, Robert, Tom, Gary, Jorner, Kjell, Willes, John, Thiede, Luca A, Kundaje, Anshul, Aspuru-Guzik, Alan

“…The efficient exploration of chemical space to design molecules with intended properties enables the accelerated discovery of drugs, materials, and catalysts,…”
Get full text

Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space by Nigam, AkshatKumar, Friederich, Pascal, Krenn, Mario, Aspuru-Guzik, Alán

“…International Conference on Learning Representations (ICLR-2020) Challenges in natural sciences can often be phrased as optimization problems. Machine learning…”
Get full text

Quantum Computing-Enhanced Algorithm Unveils Novel Inhibitors for KRAS by Vakili, Mohammad Ghazi, Gorgulla, Christoph, Nigam, AkshatKumar, Bezrukov, Dmitry, Varoli, Daniel, Aliper, Alex, Polykovsky, Daniil, Das, Krishna M. Padmanabha, Snider, Jamie, Lyakisheva, Anna, Mansob, Ardalan Hosseini, Yao, Zhong, Bitar, Lela, Radchenko, Eugene, Ding, Xiao, Liu, Jinxin, Meng, Fanye, Ren, Feng, Cao, Yudong, Stagljar, Igor, Aspuru-Guzik, Alán, Zhavoronkov, Alex

“…The discovery of small molecules with therapeutic potential is a long-standing challenge in chemistry and biology. Researchers have increasingly leveraged…”
Get full text

Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation by Krenn, Mario, Häse, Florian, Nigam, AkshatKumar, Friederich, Pascal, Aspuru-Guzik, Alán

“…Machine Learning: Science and Technology 1, 045024 (2020) The discovery of novel materials and functional molecules can help to solve some of society's most…”
Get full text