1 code implementation • • Hirofumi Tsuruta, Hiroyuki Yamazaki, Ryota Maeda, Ryotaro Tamura, Jennifer N. Wei, Zelda Mariet, Poomarin Phloyphisut, Hidetoshi Shimokawa, Joseph R. Ledsam, Lucy Colwell, Akihiro Imura
However, the publicly available datasets in existing works have notable limitations, such as small sizes and the lack of non-binding samples and exact amino acid sequences.
In this study we derive ML models from over 50 fragment-screening campaigns to introduce two important elements that we believe are absent in most -- if not all -- ML studies of this type reported to date: First, alongside the observed hits we use to train our models, we incorporate true misses and show that these experimentally validated negative data are of significant importance to the quality of the derived models.
12 code implementations • • Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Davis, Afroz Mohiuddin, Lukasz Kaiser, David Belanger, Lucy Colwell, Adrian Weller
We introduce Performers, Transformer architectures which can estimate regular (softmax) full-rank-attention Transformers with provable accuracy, but using only linear (as opposed to quadratic) space and time complexity, without relying on any priors such as sparsity or low-rankness.
Ranked #15 on Image Generation on ImageNet 64x64 (Bits per dim metric)
Our results confirm that high-performing models may not learn the correct binding rule, and suggest concrete steps that can remedy this situation.
The cost and latency of wet-lab experiments requires methods that find good sequences in few experimental rounds of large batches of sequences--a setting that off-the-shelf black-box optimization methods are ill-equipped to handle.
1 code implementation • 5 Jun 2020 • Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Davis, David Belanger, Lucy Colwell, Adrian Weller
In response, solutions that exploit the structure and sparsity of the learned attention matrix have blossomed.
In response, we propose using reinforcement learning (RL) based on proximal-policy optimization (PPO) for biological sequence design.
Capturing the microscopic interactions that determine molecular reactivity poses a challenge across the physical sciences.
The dataset bias makes these models unreliable for accurately revealing information about the mechanisms of protein-ligand binding.