Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Jamasb, Arian"'
Autor:
Tagasovska, Nataša, Park, Ji Won, Kirchmeyer, Matthieu, Frey, Nathan C., Watkins, Andrew Martin, Ismail, Aya Abdelsalam, Jamasb, Arian Rokkum, Lee, Edith, Bryson, Tyler, Ra, Stephen, Cho, Kyunghyun
Machine learning (ML) has demonstrated significant promise in accelerating drug design. Active ML-guided optimization of therapeutic molecules typically relies on a surrogate model predicting the target property of interest. The model predictions are
Externí odkaz:
http://arxiv.org/abs/2407.21028
Autor:
Jamasb, Arian R., Morehead, Alex, Joshi, Chaitanya K., Zhang, Zuobai, Didi, Kieran, Mathis, Simon V., Harris, Charles, Tang, Jian, Cheng, Jianlin, Lio, Pietro, Blundell, Tom L.
We introduce ProteinWorkshop, a comprehensive benchmark suite for representation learning on protein structures with Geometric Graph Neural Networks. We consider large-scale pre-training and downstream tasks on both experimental and predicted structu
Externí odkaz:
http://arxiv.org/abs/2406.13864
Autor:
Anand, Rishabh, Joshi, Chaitanya K., Morehead, Alex, Jamasb, Arian R., Harris, Charles, Mathis, Simon V., Didi, Kieran, Hooi, Bryan, Liò, Pietro
We introduce RNA-FrameFlow, the first generative model for 3D RNA backbone design. We build upon SE(3) flow matching for protein backbone generation and establish protocols for data preparation and evaluation to address unique challenges posed by RNA
Externí odkaz:
http://arxiv.org/abs/2406.13839
We present VoxBind, a new score-based generative model for 3D molecules conditioned on protein structures. Our approach represents molecules as 3D atomic density grids and leverages a 3D voxel-denoising network for learning and generation. We extend
Externí odkaz:
http://arxiv.org/abs/2405.03961
Autor:
Harris, Charles, Didi, Kieran, Jamasb, Arian R., Joshi, Chaitanya K., Mathis, Simon V., Lio, Pietro, Blundell, Tom
Deep generative models for structure-based drug design (SBDD), where molecule generation is conditioned on a 3D protein pocket, have received considerable interest in recent years. These methods offer the promise of higher-quality molecule generation
Externí odkaz:
http://arxiv.org/abs/2308.07413
Autor:
Joshi, Chaitanya K., Jamasb, Arian R., Viñas, Ramon, Harris, Charles, Mathis, Simon V., Morehead, Alex, Anand, Rishabh, Liò, Pietro
Computational RNA design tasks are often posed as inverse problems, where sequences are designed based on adopting a single desired secondary structure without considering 3D geometry and conformational diversity. We introduce gRNAde, a geometric RNA
Externí odkaz:
http://arxiv.org/abs/2305.14749
Autor:
Griffiths, Ryan-Rhys, Klarner, Leo, Moss, Henry B., Ravuri, Aditya, Truong, Sang, Stanton, Samuel, Tom, Gary, Rankovic, Bojana, Du, Yuanqi, Jamasb, Arian, Deshwal, Aryan, Schwartz, Julius, Tripp, Austin, Kell, Gregory, Frieder, Simon, Bourached, Anthony, Chan, Alex, Moss, Jacob, Guo, Chengzhi, Durholt, Johannes, Chaurasia, Saudamini, Strieth-Kalthoff, Felix, Lee, Alpha A., Cheng, Bingqing, Aspuru-Guzik, Alán, Schwaller, Philippe, Tang, Jian
We introduce GAUCHE, a library for GAUssian processes in CHEmistry. Gaussian processes have long been a cornerstone of probabilistic machine learning, affording particular advantages for uncertainty quantification and Bayesian optimisation. Extending
Externí odkaz:
http://arxiv.org/abs/2212.04450
Autor:
Schneuing, Arne, Harris, Charles, Du, Yuanqi, Didi, Kieran, Jamasb, Arian, Igashov, Ilia, Du, Weitao, Gomes, Carla, Blundell, Tom, Lio, Pietro, Welling, Max, Bronstein, Michael, Correia, Bruno
Structure-based drug design (SBDD) aims to design small-molecule ligands that bind with high affinity and specificity to pre-determined protein targets. Generative SBDD methods leverage structural data of drugs in complex with their protein targets t
Externí odkaz:
http://arxiv.org/abs/2210.13695
Autor:
Zhang, Zuobai, Xu, Minghao, Jamasb, Arian, Chenthamarakshan, Vijil, Lozano, Aurelie, Das, Payel, Tang, Jian
Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein function or structure. Existing approaches usually pretrain protein language models on a large number of unlabeled amino acid sequences
Externí odkaz:
http://arxiv.org/abs/2203.06125
Structure-based drug design involves finding ligand molecules that exhibit structural and chemical complementarity to protein pockets. Deep generative methods have shown promise in proposing novel molecules from scratch (de-novo design), avoiding exh
Externí odkaz:
http://arxiv.org/abs/2111.04107