BindingDB in 2024: a FAIR knowledgebase of protein-small molecule binding data DOI Creative Commons

Tiqing Liu,

Linda Hwang,

S.K. Burley

и другие.

Nucleic Acids Research, Год журнала: 2024, Номер 53(D1), С. D1633 - D1644

Опубликована: Ноя. 22, 2024

Abstract BindingDB (bindingdb.org) is a public, web-accessible database of experimentally measured binding affinities between small molecules and proteins, which supports diverse applications including medicinal chemistry, biochemical pathway annotation, training artificial intelligence models computational chemistry methods development. This update reports significant growth enhancements since our last review in 2016. Of note, the now contains 2.9 million measurements spanning 1.3 compounds thousands protein targets. largely attributable to unique focus on curating data from US patents, has yielded substantial influx novel data. Recent improvements include remake website following responsive web design principles, enhanced search filtering capabilities, new download options webservices establishment long-term archive replicated across dispersed sites. We also discuss BindingDB’s positioning relative related resources, its open sharing policies, insights gleaned dataset plans for future

Язык: Английский

Sample efficient reinforcement learning with active learning for molecular design DOI Creative Commons
Michael Dodds, Jeff Guo, Thomas Löhr

и другие.

Chemical Science, Год журнала: 2024, Номер 15(11), С. 4146 - 4160

Опубликована: Янв. 1, 2024

Reinforcement learning (RL) is a powerful and flexible paradigm for searching solutions in high-dimensional action spaces. However, bridging the gap between playing computer games with thousands of simulated episodes solving real scientific problems complex involved environments (up to actual laboratory experiments) requires improvements terms sample efficiency make most expensive information. The discovery new drugs major commercial application RL, motivated by very large nature chemical space need perform multiparameter optimization (MPO) across different properties.

Язык: Английский

Процитировано

11

The AI-driven Drug Design (AIDD) platform: an interactive multi-parameter optimization system integrating molecular evolution with physiologically based pharmacokinetic simulations DOI
Jeremy O. Jones, Robert D. Clark,

Michael S. Lawless

и другие.

Journal of Computer-Aided Molecular Design, Год журнала: 2024, Номер 38(1)

Опубликована: Март 19, 2024

Язык: Английский

Процитировано

11

Large Language Models for Inorganic Synthesis Predictions DOI
Seong-Min Kim, Yousung Jung, Joshua Schrier

и другие.

Journal of the American Chemical Society, Год журнала: 2024, Номер 146(29), С. 19654 - 19659

Опубликована: Июль 11, 2024

We evaluate the effectiveness of pretrained and fine-tuned large language models (LLMs) for predicting synthesizability inorganic compounds selection precursors needed to perform synthesis. The predictions LLMs are comparable to─and sometimes better than─recent bespoke machine learning these tasks but require only minimal user expertise, cost, time develop. Therefore, this strategy can serve both as an effective strong baseline future studies various chemical applications a practical tool experimental chemists.

Язык: Английский

Процитировано

11

Efficacious human metapneumovirus vaccine based on AI-guided engineering of a closed prefusion trimer DOI Creative Commons
Mark J. G. Bakkers, Tina Ritschel,

Machteld M. Tiemessen

и другие.

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Июль 25, 2024

The prefusion conformation of human metapneumovirus fusion protein (hMPV Pre-F) is critical for eliciting the most potent neutralizing antibodies and preferred immunogen an efficacious vaccine against hMPV respiratory infections. Here we show that additional cleavage event in F allows closure correct folding trimer. We therefore engineered to undergo double cleavage, which enabled screening Pre-F stabilizing substitutions at natively folded protomer interfaces. To identify these substitutions, developed AI convolutional classifier successfully predicts complex polar interactions often overlooked by physics-based methods visual inspection. combination processing, stabilization interface regions membrane-proximal stem, resulted a candidate without need heterologous trimerization domain exhibited high expression yields thermostability. Cryo-EM analysis shows complete ectodomain structure, including specific interaction newly identified cleaved C-terminus with adjacent protomer. Importantly, induces cross-neutralizing antibody responses resulting near protection challenge cotton rats, making highly stable, double-cleaved trimer attractive candidate.

Язык: Английский

Процитировано

11

BindingDB in 2024: a FAIR knowledgebase of protein-small molecule binding data DOI Creative Commons

Tiqing Liu,

Linda Hwang,

S.K. Burley

и другие.

Nucleic Acids Research, Год журнала: 2024, Номер 53(D1), С. D1633 - D1644

Опубликована: Ноя. 22, 2024

Abstract BindingDB (bindingdb.org) is a public, web-accessible database of experimentally measured binding affinities between small molecules and proteins, which supports diverse applications including medicinal chemistry, biochemical pathway annotation, training artificial intelligence models computational chemistry methods development. This update reports significant growth enhancements since our last review in 2016. Of note, the now contains 2.9 million measurements spanning 1.3 compounds thousands protein targets. largely attributable to unique focus on curating data from US patents, has yielded substantial influx novel data. Recent improvements include remake website following responsive web design principles, enhanced search filtering capabilities, new download options webservices establishment long-term archive replicated across dispersed sites. We also discuss BindingDB’s positioning relative related resources, its open sharing policies, insights gleaned dataset plans for future

Язык: Английский

Процитировано

11