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Protocol to generate dual-target compounds using a transformer chemical language model

STAR PROTOCOLS 2025年第1期6卷 103584页

作者： Srinivasan, Sanjana Bajorath, Juergen Rhein Friedrich Wilhelms Univ Dept Life Sci Informat & Data Sci LIMES Program Unit Chem Biol & Med Chem B IT Friedrich Hirzebruch Allee 5-6 D-53115 Bonn Germany Lamarr Inst Machine Learning & Artificial Intellig Friedrich Hirzebruch Allee 5-6 D-53115 Bonn Germany

Here, we present a protocol to generate dual-target compounds (DT-CPDs) interacting with two distinct target proteins using a transformer-based chemical language model. We describe steps for installing software, preparing data, and pre-training the model on pairs of single-target compounds (ST-CPDs), which bind to an individual protein, and DT-CPDs. We then detail procedures for assembling ST- and corresponding DT-CPD data for specific protein pairs and evaluating the model's performance on hold-out test sets. For complete details on the use and execution of this protocol, please refer to Srinivasan and Bajorath.1

关键词： Molecular/Chemical Probes Systems biology Chemistry Computer sciences

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PeakCNV: A multi-feature ranking algorithm-based tool for genome-wide copy number variation-association study

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COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 2022年 20卷 4975-4983页

作者： Labani, Mahdieh Afrasiabi, Ali Beheshti, Amin Lovell, Nigel H. Alinejad-Rokny, Hamid UNSW Sydney Grad Sch Biomed Engn Biomed Machine Learning Lab BML Sydney NSW 2052 Australia Macquarie Univ Sch Comp Data Analyt Lab Sydney NSW 2109 Australia UNSW Sydney Grad Sch Biomed Engn GSBmE Sydney NSW 2052 Australia UNSW Sydney Tyree Inst Hlth Engn IHealthE Sydney NSW 2052 Australia Univ New South Wales UNSW Data Sci Hub Sydney NSW 2052 Australia Macquarie Univ AI Enabled Proc AIP Res Ctr Hlth Data Analyt Program Sydney 2109 Australia Macquarie Univ Sch Comp Sydney NSW Australia

Copy Number Variation (CNV) refers to a type of structural genomic alteration in which a segment of chromosome is duplicated or deleted. To date, many CNVs have been identified as causative genetic ele-ments for several diseases and phenotypes. However, performing a CNV-based genome-wide association study is challenging due to inconsistency in length and occurrence of CNVs across different individuals under investigation. One of the most efficient strategies to address this issue is building CNV regions (ge-nomic regions in which CNVs are overlapping -CNVRs). However, this approach is susceptible to a high false positive rate due to overlapping and co-occurring of confounding CNVRs with true positive CNVRs. Here, we develop PeakCNV that differentiates false-positive CNVRs from true positives by calculating a new metric, independence ranking score, (IR-score) via a feature ranking approach. We compared the performance of PeakCNV with other current existing tools by carrying out two case studies one using the CNV genotype data for individuals with prostate cancer (194 cases and 2,392 healthy individuals) and the second one for individuals with neurodevelopmental disorders (19,642 cases and 6,451 healthy individuals). Crucially, our benchmarking analyses on prostate cancer cohort indicated that PeakCNV identifies a fewer risk candidate CNVRs with shorter lengths compared to other tools. Importantly, these CNVRs cover a greater proportion of case over healthy individuals compared to other tools. The accuracy of PeakCNV in identifying relevant candidate CNVRs was reproducible in the case study on neurodevel-opmental disorders. Using data from the FANTOM5 expression atlas and the Clinical Genomic Database, we show that the candidate CNVRs identified by PeakCNV for neurodevelopmental disorders overlap with a greater number of genes with the brain-enriched expression, and a greater number of genes that are associated with neurological conditions compared to candidate CNVRs

关键词： Genetic abnormalities Copy number variations CNV association study Risk genes Artificial intelligence

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Generation of dual-target compounds using a transformer chemical language model

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CELL REPORTS PHYSICAL SCIENCE 2024年第11期5卷

作者： Srinivasan, Sanjana Bajorath, Juergen Univ Bonn Dept Life Sci Informat & Data Sci B IT Friedrich-Hirzebruch-Allee 5-6 D-53115 Bonn Germany Univ Bonn Lamarr Inst Machine Learning & Artificial Intellig Friedrich-Hirzebruch-Allee 5-6 D-53115 Bonn Germany Univ Bonn Limes Inst Program Unit Chem Biol & Med Chem Friedrich-Hirzebruch-Allee 5-6 D-53115 Bonn Germany

Compounds with defined multi-target activity are candidates for the treatment of multi-factorial diseases. Such compounds are mostly discovered experimentally. Designing compounds with the desired activity against two targets is typically attempted by pharmacophore fusion. In addition, machine learning models can be derived for multi-target prediction of compounds or computational target profiling. Here, we introduce transformer-based chemical language model variants for the generative design of dual-target compounds. Alternative models were pre-trained by learning mappings of single- to dual-target compounds of increasing similarity. Different models were optimized for generating compounds with activity against pairs of functionally unrelated targets using a new cross-fine-tuning approach. Control models confirmed that pre-trained and finetuned models learned the chemical space of dual-target compounds. The final models were found to exactly reproduce known dual-target compounds excluded from model derivation. In addition, many structural analogs of such compounds were generated, further supporting the validity of the methodology.

关键词： dual-target compounds polypharmacology generative design transformer chemical language model cross fine-tuning

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machine learning models with distinct Shapley and interpretation for chemical compound predictions

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CELL REPORTS PHYSICAL SCIENCE 2024年第8期5卷

作者： Roth, Jannik P. Bajorath, Juergen Univ Bonn Dept Life Sci Informat & Data Sci B IT Friedrich Hirzebruch Allee 5-6 D-53115 Bonn Germany Univ Bonn Lamarr Inst Machine Learning & Artificial Intellig Friedrich Hirzebruch Allee 5-6 D-53115 Bonn Germany Univ Bonn Limes Inst Program Unit Chem Biol & Med Chem Friedrich Hirzebruch Allee 5-6 D-53115 Bonn Germany

Explaining black box predictions of machine learning (ML) models is a topical issue in artificial intelligence (AI) research. For the identification of features determining predictions, the Shapley value formalism originally developed in game theory is widely used in different fields. Typically, Shapley values quantifying feature contributions to predictions need to be approximated in machine learning. We introduce a framework for the calculation of exact Shapley values for 4 kernel functions used in support vector machine (SVM) models and analyze consistently accurate compound activity predictions based on exact Shapley values. Dramatic changes in feature contributions are detected depending on the kernel function, leading to mostly distinct explanations of predictions of the same test compounds. Very different feature contributions yield comparable predictions, which complicate numerical and graphical model explanation and decouple feature attribution and human interpretability.

关键词： machine learning explainable artificial intelligence feature attribution exact Shapley values support vector machine model interpretation

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Uncovering and tackling fundamental limitations of compound potency predictions using machine learning models

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CELL REPORTS PHYSICAL SCIENCE 2024年第6期5卷

作者： Janela, Tiago Bajorath, Juergen Univ Bonn Dept Life Sci Informat & Data Sci B IT Friedrich Hirzebruch Allee 5-6 D-53115 Bonn Germany Univ Bonn Lamarr Inst Machine Learning & Artificial Intellig Friedrich Hirzebruch Allee 5-6 D-53115 Bonn Germany Univ Bonn Limes Inst Program Unit Chem Biol & Med Chem B IT Friedrich Hirzebruch Allee 5-6 D-53115 Bonn Germany

Molecular property predictions play a central role in computer-aided drug discovery. Although a variety of physicochemical (e.g., solubility or chemical reactivity) or physiological properties (e.g., metabolic stability or toxicity) can be predicted, biological activity is by far the most frequently investigated compound feature. Activity predictions are carried out in a qualitative (target-based activity, through compound classification) or quantitative (compound potency or studies have evaluated and compared different machine learning methods for activity and potency predictions, recently with a focus on deep learning. Regardless of the methods used, these studies generally rely on conventional benchmark settings. Recent work has shown that potency prediction benchmarks have severe general limitations that have long been unnoticed but prevent a reliable assessment of different methods and their relative performance. In this perspective, we outline general limitations of benchmark settings for compound potency predictions, introduce potential alternatives enabling a more realistic assessment of state-of-the-art predictive models, and discuss future directions for elucidating predictions and further increasing their impact.

关键词： compound potency prediction drug design compound activity classes machine learning performance evaluation data augmentation

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A multi-modal scanning system to digitize CBRNE emergency response scenes

A multi-modal scanning system to digitize CBRNE emergency re...

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IEEE International Workshop on Safety, Security, and Rescue Robotics (SSRR)

作者： Marco Salathe Brian J. Quiter Mark S. Bandstra Xin Chen Victor Negut Micah Folsom Gunther H. Weber Christopher Greulich Mathew Swinney Nicholas Prins Daniel E. Archer Applied Nuclear Physics Program at Lawrence Berkeley National Laboratory Berkeley CA USA Machine Learning and Analytics group Computer Science Research Division Lawrence Berkeley National Laboratory Oak Ridge National Laboratory Oak Ridge TN USA

ISBN: (纸本)9781665456814

A handheld system developed to digitize a contextual understanding of the scene at a chemical, biological, radiological, nuclear and/or explosives (CBRNE) events is described. The system uses LiDAR and cameras to create a colorized 3D model of the environment, which helps domain experts that are supporting responders in the field. To generate the digitized model, a responder scans any suspicious objects and the surroundings by carrying the system through the scene. The scanning system provides a real-time user interface to inform the user about scanning progress and to indicate any areas that may have been missed either by the LiDAR sensors or the cameras. Currently, the collected data are post-processed on a different device, building a colorized triangular mesh of the encountered scene, with the intention of moving this pipeline to the scanner at a later point. The mesh is sufficiently compressed to be sent over a reduced bandwidth connection to a remote analyst. Furthermore, the system tracks fiducial markers attached to diagnostic equipment that is placed around the suspicious object. The resulting tracking information can be transmitted to remote analysts to further facilitate their supporting efforts. The paper will discuss the system's design, software components, the user interface used for scanning a scene, the necessary procedures for calibration of the sensors, and the processing steps of the resulting data. The discussion will close by evaluating the system's performance on 11 scenes.

关键词： Computer vision Three-dimensional displays Laser radar Computational modeling Pipelines User interfaces Position measurement

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Deep Reinforcement learning for RAN Optimization and Control

Deep Reinforcement Learning for RAN Optimization and Control

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IEEE Wireless Communications and Networking Conference (WCNC)

作者： Chen, Yu Chen, Jie Krishnamurthi, Ganesh Yang, Huijing Wang, Huahui Zhao, Wenjie Carnegie Mellon Univ Neurosci Inst 5000 Forbes Ave Pittsburgh PA 15213 USA Carnegie Mellon Univ Machine Learning Dept 5000 Forbes Ave Pittsburgh PA 15213 USA AT&T Labs Summer Internship Program Atlanta GA USA AT&T Labs 1 AT&T Way Bedminster NJ USA

ISBN: (纸本)9781728195056

Due to the high variability of the traffic in the radio access network (RAN), fixed network configurations are not flexible enough to achieve optimal performance. Our vendors provide several settings of the eNodeB to optimize the RAN performance, such as media access control scheduler, loading balance, etc. But the detailed mechanisms of the eNodeB configurations are usually very complicated and not disclosed, not to mention the large key performance indicators (KPIs) space needed to be considered. These make constructing a simulator, offline tuning, or rule-based solutions difficult. We aim to build an intelligent controller without strong assumption or domain knowledge about the RAN and can run 24/7 without supervision. To achieve this goal, we first build a closed-loop control testbed RAN in a lab environment with one eNodeB provided by one of the largest wireless vendors and four smartphones. Next, we build a double Q network agent trained with the live feedback of the key performance indicators from the RAN. Our work proved the effectiveness of applying deep reinforcement learning to improve network performance in a real RAN network environment.

关键词： self-organizing network radio access networks deep reinforcement learning testbed

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Protocol to calculate and compare exact Shapley values for different kernels in support vector machine models using binary features

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STAR PROTOCOLS 2024年第4期5卷 103450页

作者： Roth, Jannik P. Bajorath, Juergen Rheinische Friedrich Wilhelms Univ Dept Life Sci Informat & Data Sci LIMES Program Unit Chem Biol & Med Chem B IT Friedrich Hirzebruch Allee 5-6 D-53115 Bonn Germany Lamarr Inst Machine Learning & Artificial Intellig Friedrich Hirzebruch Allee 5-6 D-53115 Bonn Germany

The Shapley value formalism from cooperative game theory was adapted to explain predictions of machine learning models. Here, we present a protocol to calculate and compare exact Shapley values for support vector machine models with commonly used kernels and binary input features. We describe steps for installing software, preparing data, and calculating Shapley values with customizable Python scripts. We then detail procedures for analyzing results via correlation analysis and feature mapping. For complete details on the use and execution of this protocol, please refer to Roth and Bajorath.1

关键词： bioinformatics chemistry computer sciences

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Deriving spatial features from in situ proteomics imaging to enhance cancer survival analysis

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BIOINFORMATICS 2023年第Sup1期39卷 I140-I148页

作者： Dayao, Monica T. Trevino, Alexandro Kim, Honesty Ruffalo, Matthew D'Angio, H. Blaize Preska, Ryan Duvvuri, Umamaheswar Mayer, Aaron T. Bar-Joseph, Ziv Univ Pittsburgh Joint Carnegie Mellon Univ Program Computat Biol Pittsburgh PA 15213 USA Carnegie Mellon Univ Computat Biol Dept Pittsburgh PA 15213 USA Enable Med Menlo Pk CA 94025 USA Univ Pittsburgh Dept Otolaryngol Pittsburgh PA 15213 USA Carnegie Mellon Univ Machine Learning Dept Pittsburgh PA 15213 USA

MotivationSpatial proteomics data have been used to map cell states and improve our understanding of tissue organization. More recently, these methods have been extended to study the impact of such organization on disease progression and patient survival. However, to date, the majority of supervised learning methods utilizing these data types did not take full advantage of the spatial information, impacting their performance and *** inspiration from ecology and epidemiology, we developed novel spatial feature extraction methods for use with spatial proteomics data. We used these features to learn prediction models for cancer patient survival. As we show, using the spatial features led to consistent improvement over prior methods that used the spatial proteomics data for the same task. In addition, feature importance analysis revealed new insights about the cell interactions that contribute to patient survival.

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Frequency Bias Causes Overestimation of Climate Change Impacts on Global Flood Occurrence

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GEOPHYSICAL RESEARCH LETTERS 2024年第16期51卷 e2024GL108855-e2024GL108855页

作者： Zhao, Fang Lange, Stefan Goswami, Bedartha Frieler, Katja East China Normal Univ Sch Geog Sci Minist Educ Key Lab Geog Informat Sci Shanghai Peoples R China Minist Nat Resources Key Lab Spatial temporal Big Data Anal & Applicat Shanghai Peoples R China Leibniz Assoc Potsdam Inst Climate Impact Res PIK Potsdam Germany Int Inst Appl Syst Anal IIASA Biodivers & Nat Resources Program Water Secur Res Grp Laxenburg Austria Univ Tubingen Machine Learning Climate Sci Tubingen Germany

The frequency change of 100-year flood events is often determined by fitting extreme value distributions to annual maximum discharge from a historical base period. This study demonstrates that this approach may significantly bias the computed flood frequency change. An idealized experiment shows frequency bias exceeding 100% for a 50-year base period. Further analyses using Monte Carlo simulations, mathematical derivations, and hydrological model outputs reveal that bias magnitude inversely relates to base period length and is weakly influenced by the generalized extreme value distribution's shape parameter. The bias, persisting across different estimation methods, implies floods may exceed local defenses designed based on short historical records more often than expected, even without climate change. We introduce a frequency bias adjustment method, which significantly reduces the projected rise in global flood occurrence. This suggests a substantial part of the earlier projected increase in flood occurrence and impacts is not attributable to climate change. We report a previously overlooked bias in a common method for projecting the frequency of extreme floods, like 100-year events. This method typically uses a short period of historical data for two primary purposes: first, to define local thresholds for extreme floods, such as 100-year events, and second, to serve as a base for projecting future changes in flood occurrences. Our study, utilizing random data, demonstrates that this method can lead to inaccurate projections, increasing the number of expected extreme floods in future years and decreasing them in historical baseline years, even when no change is indicated by the data. Additionally, we find that the longer the historical period used, the smaller the bias. By simulating the characteristics of real hydrological model projections with artificial data, we are able to estimate a bias similar to that observed in these projections, and thus propose a simple

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