Digital discovery最新文献

A workflow to create a high-quality protein-ligand binding dataset for training, validation, and prediction tasks. 为训练、验证和预测任务创建高质量蛋白质配体结合数据集的工作流程。

IF 6.2

Digital discovery Pub Date : 2025-04-02 DOI: 10.1039/d4dd00357h

Yingze Wang, Kunyang Sun, Jie Li, Xingyi Guan, Oufan Zhang, Dorian Bagni, Yang Zhang, Heather A Carlson, Teresa Head-Gordon

{"title":"A workflow to create a high-quality protein-ligand binding dataset for training, validation, and prediction tasks.","authors":"Yingze Wang, Kunyang Sun, Jie Li, Xingyi Guan, Oufan Zhang, Dorian Bagni, Yang Zhang, Heather A Carlson, Teresa Head-Gordon","doi":"10.1039/d4dd00357h","DOIUrl":"10.1039/d4dd00357h","url":null,"abstract":"Development of scoring functions (SFs) used to predict protein-ligand binding energies requires high-quality 3D structures and binding assay data for training and testing their parameters. In this work, we show that one of the widely-used datasets, PDBbind, suffers from several common structural artifacts of both proteins and ligands, which may compromise the accuracy, reliability, and generalizability of the resulting SFs. Therefore, we have developed a series of algorithms organized in a semi-automated workflow, HiQBind-WF, that curates non-covalent protein-ligand datasets to fix these problems. We also used this workflow to create an independent data set, HiQBind, by matching binding free energies from various sources including BioLiP, Binding MOAD and Binding DB with co-crystalized ligand-protein complexes from the PDB. The resulting HiQBind workflow and dataset are designed to ensure reproducibility and to minimize human intervention, while also being open-source to foster transparency in the improvements made to this important resource for the biology and drug discovery communities.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" ","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11967698/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143796976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving structural plausibility in diffusion-based 3D molecule generation via property-conditioned training with distorted molecules† 通过扭曲分子的属性条件训练提高基于扩散的三维分子生成的结构合理性

IF 6.2

Digital discovery Pub Date : 2025-03-24 DOI: 10.1039/D4DD00331D

Lucy Vost, Vijil Chenthamarakshan, Payel Das and Charlotte M. Deane

{"title":"Improving structural plausibility in diffusion-based 3D molecule generation via property-conditioned training with distorted molecules†","authors":"Lucy Vost, Vijil Chenthamarakshan, Payel Das and Charlotte M. Deane","doi":"10.1039/D4DD00331D","DOIUrl":"https://doi.org/10.1039/D4DD00331D","url":null,"abstract":"Traditional drug design methods are costly and time-consuming due to their reliance on trial-and-error processes. As a result, computational methods, including diffusion models, designed for molecule generation tasks have gained significant traction. Despite their potential, they have faced criticism for producing physically implausible outputs. As a solution to this problem, we propose a conditional training framework resulting in a model capable of generating molecules of varying and controllable levels of structural plausibility. This framework consists of adding distorted molecules to training datasets, and then annotating each molecule with a label representing the extent of its distortion, and hence its quality. By training the model to distinguish between favourable and unfavourable molecular conformations alongside the standard molecule generation training process, we can selectively sample molecules from the high-quality region of learned space, resulting in improvements in the validity of generated molecules. In addition to the standard two datasets used by molecule generation methods (QM9 and GEOM), we also test our method on a druglike dataset derived from ZINC. We use our conditional method with EDM, the first E(3) equivariant diffusion model for molecule generation, as well as two further models—a more recent diffusion model and a flow matching model—which were built off EDM. We demonstrate improvements in validity as assessed by RDKit parsability and the PoseBusters test suite; more broadly, though, our findings highlight the effectiveness of conditioning methods on low-quality data to improve the sampling of high-quality data.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 4","pages":" 1092-1099"},"PeriodicalIF":6.2,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/dd/d4dd00331d?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient strategies for reducing sampling error in quantum Krylov subspace diagonalization 量子Krylov子空间对角化中降低采样误差的有效策略

IF 6.2

Digital discovery Pub Date : 2025-03-21 DOI: 10.1039/D4DD00321G

Gwonhak Lee, Seonghoon Choi, Joonsuk Huh and Artur F. Izmaylov

{"title":"Efficient strategies for reducing sampling error in quantum Krylov subspace diagonalization","authors":"Gwonhak Lee, Seonghoon Choi, Joonsuk Huh and Artur F. Izmaylov","doi":"10.1039/D4DD00321G","DOIUrl":"https://doi.org/10.1039/D4DD00321G","url":null,"abstract":"Within the realm of early fault-tolerant quantum computing (EFTQC), quantum Krylov subspace diagonalization (QKSD) has emerged as a promising quantum algorithm for the approximate Hamiltonian diagonalization via projection onto the quantum Krylov subspace. However, the algorithm often requires solving an ill-conditioned generalized eigenvalue problem (GEVP) involving erroneous matrix pairs, which can significantly distort the solution. Since EFTQC assumes limited-scale error correction, finite sampling error becomes a dominant source of error in these matrices. This work focuses on quantifying sampling errors during the measurement of matrix element in the projected Hamiltonian examining two measurement approaches based on the Hamiltonian decompositions: the linear combination of unitaries and diagonalizable fragments. To reduce sampling error within a fixed budget of quantum circuit repetitions, we propose two measurement strategies: the shifting technique and coefficient splitting. The shifting technique eliminates redundant Hamiltonian components that annihilate either the bra or ket states, while coefficient splitting optimizes the measurement of common terms across different circuits. Numerical experiments with electronic structures of small molecules demonstrate the effectiveness of these strategies, reducing sampling costs by a factor of 20–500.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 4","pages":" 954-969"},"PeriodicalIF":6.2,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/dd/d4dd00321g?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

pyRheo: an open-source Python package for complex rheology† pyRheo：一个用于复杂流变学的开源Python包

IF 6.2

Digital discovery Pub Date : 2025-03-20 DOI: 10.1039/D5DD00021A

Isaac Y. Miranda-Valdez, Aaro Niinistö, Tero Mäkinen, Juha Lejon, Juha Koivisto and Mikko J. Alava

引用次数: 0

Robotic integration for end-stations at scientific user facilities† 科学用户设施终端站机器人集成*

IF 6.2

Digital discovery Pub Date : 2025-03-20 DOI: 10.1039/D5DD00036J

Chandima Fernando, Hailey Marcello, Jakub Wlodek, John Sinsheimer, Daniel Olds, Stuart I. Campbell and Phillip M. Maffettone

{"title":"Robotic integration for end-stations at scientific user facilities†","authors":"Chandima Fernando, Hailey Marcello, Jakub Wlodek, John Sinsheimer, Daniel Olds, Stuart I. Campbell and Phillip M. Maffettone","doi":"10.1039/D5DD00036J","DOIUrl":"https://doi.org/10.1039/D5DD00036J","url":null,"abstract":"The integration of robotics and artificial intelligence (AI) into scientific workflows is transforming experimental research, particularly at large-scale user facilities such as the National Synchrotron Light Source II (NSLS-II). We present an extensible architecture for robotic sample management that combines the Robot Operating System 2 (ROS2) with the Bluesky experiment orchestration ecosystem. This approach enabled seamless integration of robotic systems into high-throughput experiments and adaptive workflows. Key innovations included a client-server model for managing robotic actions, real-time pose estimation using fiducial markers and computer vision, and closed-loop adaptive experimentation with agent-driven decision-making. Deployed using widely available hardware and open-source software, this architecture successfully automated a full shift (8 hours) of sample manipulation without errors. The system's flexibility and extensibility allow rapid re-deployment across different experimental environments, enabling scalable self-driving experiments for end stations at scientific user facilities. This work highlights the potential of robotics to enhance experimental throughput and reproducibility, providing a roadmap for future developments in automated scientific discovery where flexibility, extensibility, and adaptability are core requirements.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 4","pages":" 1083-1091"},"PeriodicalIF":6.2,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/dd/d5dd00036j?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Measurements with noise: Bayesian optimization for co-optimizing noise and property discovery in automated experiments† 测量噪声：贝叶斯优化共同优化噪声和自动实验中的属性发现†

IF 6.2

Digital discovery Pub Date : 2025-03-17 DOI: 10.1039/D4DD00391H

Boris N. Slautin, Yu Liu, Jan Dec, Vladimir V. Shvartsman, Doru C. Lupascu, Maxim A. Ziatdinov and Sergei V. Kalinin

{"title":"Measurements with noise: Bayesian optimization for co-optimizing noise and property discovery in automated experiments†","authors":"Boris N. Slautin, Yu Liu, Jan Dec, Vladimir V. Shvartsman, Doru C. Lupascu, Maxim A. Ziatdinov and Sergei V. Kalinin","doi":"10.1039/D4DD00391H","DOIUrl":"https://doi.org/10.1039/D4DD00391H","url":null,"abstract":"We have developed a Bayesian optimization (BO) workflow that integrates intra-step noise optimization into automated experimental cycles. Traditional BO approaches in automated experiments focus on optimizing experimental trajectories but often overlook the impact of measurement noise on data quality and cost. Our proposed framework simultaneously optimizes both the target property and the associated measurement noise by introducing time as an additional input parameter, thereby balancing the signal-to-noise ratio and experimental duration. Two approaches are explored: a reward-driven noise optimization and a double-optimization acquisition function, both enhancing the efficiency of automated workflows by considering noise and cost within the optimization process. We validate our method through simulations and real-world experiments using Piezoresponse Force Microscopy (PFM), demonstrating the successful optimization of measurement duration and property exploration. Our approach offers a scalable solution for optimizing multiple variables in automated experimental workflows, improving data quality, and reducing resource expenditure in materials science and beyond.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 4","pages":" 1066-1074"},"PeriodicalIF":6.2,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/dd/d4dd00391h?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GraphXForm: graph transformer for computer-aided molecular design† GraphXForm：用于计算机辅助分子设计的图形转换器

IF 6.2

Digital discovery Pub Date : 2025-03-14 DOI: 10.1039/D4DD00339J

Jonathan Pirnay, Jan G. Rittig, Alexander B. Wolf, Martin Grohe, Jakob Burger, Alexander Mitsos and Dominik G. Grimm

{"title":"GraphXForm: graph transformer for computer-aided molecular design†","authors":"Jonathan Pirnay, Jan G. Rittig, Alexander B. Wolf, Martin Grohe, Jakob Burger, Alexander Mitsos and Dominik G. Grimm","doi":"10.1039/D4DD00339J","DOIUrl":"https://doi.org/10.1039/D4DD00339J","url":null,"abstract":"Generative deep learning has become pivotal in molecular design for drug discovery, materials science, and chemical engineering. A widely used paradigm is to pretrain neural networks on string representations of molecules and fine-tune them using reinforcement learning on specific objectives. However, string-based models face challenges in ensuring chemical validity and enforcing structural constraints like the presence of specific substructures. We propose to instead combine graph-based molecular representations, which can naturally ensure chemical validity, with transformer architectures, which are highly expressive and capable of modeling long-range dependencies between atoms. Our approach iteratively modifies a molecular graph by adding atoms and bonds, which ensures chemical validity and facilitates the incorporation of structural constraints. We present GraphXForm, a decoder-only graph transformer architecture, which is pretrained on existing compounds and then fine-tuned using a new training algorithm that combines elements of the deep cross-entropy method and self-improvement learning. We evaluate GraphXForm on various drug design tasks, demonstrating superior objective scores compared to state-of-the-art molecular design approaches. Furthermore, we apply GraphXForm to two solvent design tasks for liquid–liquid extraction, again outperforming alternative methods while flexibly enforcing structural constraints or initiating design from existing molecular structures.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 4","pages":" 1052-1065"},"PeriodicalIF":6.2,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/dd/d4dd00339j?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ligand design for 227Ac extraction by active learning and molecular topology† 基于主动学习和分子拓扑的227Ac萃取配体设计

IF 6.2

Digital discovery Pub Date : 2025-03-14 DOI: 10.1039/D5DD00007F

Jeffrey A. Laub and Konstantinos D. Vogiatzis

{"title":"Ligand design for 227Ac extraction by active learning and molecular topology†","authors":"Jeffrey A. Laub and Konstantinos D. Vogiatzis","doi":"10.1039/D5DD00007F","DOIUrl":"https://doi.org/10.1039/D5DD00007F","url":null,"abstract":"Targeted α-therapy (TAT) is a promising radiotherapeutic technique for the treatment of various cancers due to the high linear energy transfer and low penetration depth of α-particles. Unfortunately, one of the major hindrances in the use of TAT is the accessibility of acceptable α-emitting radioisotopes. Of the acceptable radioisotopes, 223Ra, 224Ra, 225Ra, and 225Ac can all originate from 227Ac. Being able to selectively isolate 227Ac is crucial for aiding in increasing the accessibility of α-emitting radioisotopes for TAT. Some of the more successful ligands used for the selective separation of trivalent actinides are the 6,6′-bis(1,2,4-triazin-3-yl)-2,2′-bipyridine (BTBP)-based ligand family. Current ligand performance screening is accomplished by using a trial-and-error-based method which is expensive and based primarily on chemical intuition and previous studies. In this study, effective computer-aided ligand screening has been accomplished by generating CyMe4–BTBP-based ligands and predicting stability constants for 227Ac extraction of each using scalar relativistic density functional theory (DFT) followed by supervised machine learning (ML). DFT was used to compute stability constants from a 2 : 1 stoichiometric ratio of BTBP to 227Ac with three nitrate ions for charge balancing as demonstrated by experimental analysis. The computed stability constants coupled with the vectorized information from the optimized BTBP molecular geometries were used for the training of ML workflows. The performance of each algorithm was determined by the validation set and the outcomes compared to the DFT stability constants. This methodology can aid radiochemists in synthesizing targeted ligands for selective isolation of 227Ac.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 4","pages":" 1100-1112"},"PeriodicalIF":6.2,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/dd/d5dd00007f?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

BitBIRCH: efficient clustering of large molecular libraries† BitBIRCH：高效的大分子文库聚类。

IF 6.2

Digital discovery Pub Date : 2025-03-13 DOI: 10.1039/D5DD00030K

Kenneth López Pérez, Vicky Jung, Lexin Chen, Kate Huddleston and Ramón Alain Miranda-Quintana

{"title":"BitBIRCH: efficient clustering of large molecular libraries†","authors":"Kenneth López Pérez, Vicky Jung, Lexin Chen, Kate Huddleston and Ramón Alain Miranda-Quintana","doi":"10.1039/D5DD00030K","DOIUrl":"10.1039/D5DD00030K","url":null,"abstract":"The widespread use of Machine Learning (ML) techniques in chemical applications has come with the pressing need to analyze extremely large molecular libraries. In particular, clustering remains one of the most common tools to dissect the chemical space. Unfortunately, most current approaches present unfavorable time and memory scaling, which makes them unsuitable to handle million- and billion-sized sets. Here, we propose to bypass these problems with a time- and memory-efficient clustering algorithm, BitBIRCH. This method uses a tree structure similar to the one found in the Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm to ensure O(N) time scaling. BitBIRCH leverages the instant similarity (iSIM) formalism to process binary fingerprints, allowing the use of Tanimoto similarity, and reducing memory requirements. Our tests show that BitBIRCH is already >1000 times faster than standard implementations of the Taylor–Butina clustering for libraries with 1 500 000 molecules. BitBIRCH increases efficiency without compromising the quality of the resulting clusters. We explore strategies to handle large sets, which we applied in the clustering of one billion molecules under 5 hours using a parallel/iterative BitBIRCH approximation.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 4","pages":" 1042-1051"},"PeriodicalIF":6.2,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11912344/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Digital discovery and the new experimental frontier 数字发现和新的实验前沿

IF 6.2

Digital discovery Pub Date : 2025-03-11 DOI: 10.1039/D5DD00029G

S. Hessam M. Mehr

{"title":"Digital discovery and the new experimental frontier","authors":"S. Hessam M. Mehr","doi":"10.1039/D5DD00029G","DOIUrl":"https://doi.org/10.1039/D5DD00029G","url":null,"abstract":"The digitisation of chemistry has had a profound effect on the field by boosting the efficiency of information retrieval and data recording, and by automating repetitive laboratory operations. Increasingly complex molecules — both known and de novo — can be rapidly accessed with unprecedented speed and reproducibility. Despite progress as measured by these quantitative productivity metrics, a qualitative transformation in the design and structure of experimentation has yet to materialise. Here, we explore digitisation's role in a larger paradigm shift in experimental chemistry not just as a means of automated execution of procedures but dynamically sensing, interpreting, and manipulating chemical processes in real-time. This paradigm shift is characterised by transitioning from single-point measurements to continuous observation; from homogeneous to spatially organised systems; and from fixed linear experimental procedures to dynamic, branched “programs” that can unfold based on real-time feedback. This shift will enable new types of objectives in experimental chemistry, such as responsiveness, adaptability and persistence, expanding beyond static quantities like product structure, yield and purity. We explore the innovations needed to enable these transitions; the open questions they raise; and how digitisation can catalyse chemistry's evolution beyond its existing confines.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 4","pages":" 892-895"},"PeriodicalIF":6.2,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/dd/d5dd00029g?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0