Dr. Mark Mackey is the Chief Scientific Officer at Molecular Architect. He discusses using molecular fields and field points to align molecules and generate 3D-QSAR models. He provides examples of building successful 3D-QSAR models for a small SARS dataset using a guided alignment and for a large NK3 receptor antagonist dataset after predicting the binding mode. Molecular Architect combines field-based alignment and QSAR modeling into an interactive tool to aid in drug design.
Tim Cheeseright, Cresset, 'Introducing Fragment Growing in FieldStere and oth...Cresset
The upcoming release of FieldStere will enable fragment growing, This presentation will introduce the current operation and results then focus on the new features that enable more complex bioisostere searching experiments.
Sparsity Normalization: Stabilizing the Expected Outputs of Deep NetworksJoonyoung Yi
The learning of deep models, in which a numerous of parameters are superimposed, is known to be a fairly sensitive process and should be carefully done through a combination of several techniques that can help to stabilize it. We introduce an additional challenge that has never been explicitly studied: the heterogeneity of sparsity at the instance level due to missing values or the innate nature of the input distribution. We confirm experimentally on the widely used benchmark datasets that this variable sparsity problem makes the output statistics of neurons unstable and makes the learning process more difficult by saturating non-linearities. We also provide the analysis of this phenomenon, and based on our analysis, we present a simple technique to prevent this issue, referred to as Sparsity Normalization (SN). Finally, we show that the performance can be significantly improved with SN on certain popular benchmark datasets, or that similar performance can be achieved with lower capacity. Especially focusing on the collaborative filtering problem where the variable sparsity issue has been completely ignored, we achieve new state-of-the-art results on Movielens 100k and 1M datasets, by simply applying Sparsity Normalization (SN).
https://arxiv.org/abs/1906.00150
ON OPTIMIZATION OF MANUFACTURING OF AN AMPLIFIER TO INCREASE DENSITY OF BIPOL...ijoejournal
In this paper we consider a possibility to increase density of bipolar heterotransistor framework an amplifier
due to decreasing of their dimensions. The considered approach based on doping of required areas of
heterostructure with specific configuration by diffusion or ion implantation. The doping finished by optimized
annealing of dopant and/or radiation defects. Analysis of redistribution of dopant with account redistribution
of radiation defects (after implantation of ions of dopant) for optimization of the above annealing
have been done by using recently introduced analytical approach. The approach gives a possibility
to analyze mass and heat transports in a heterostructure without crosslinking of solutions on interfaces
between layers of the heterostructure with account nonlinearity of these transports and variation in time of
their parameters.
Motivation entails the development of a program that automatically performs clustering and outlier detection for a wide variety of numerically represented data.
In this paper we introduce an approach to increase vertical integration of elements of transistor-transistor logic with function AND-NOT. Framework the approach we consider a heterostructure with special configuration. Several specific areas of the heterostructure should be doped by diffusion or ion implantation. Annealing of dopant and/or radiation defects should be optimized.
Tim Cheeseright, Cresset, 'Introducing Fragment Growing in FieldStere and oth...Cresset
The upcoming release of FieldStere will enable fragment growing, This presentation will introduce the current operation and results then focus on the new features that enable more complex bioisostere searching experiments.
Sparsity Normalization: Stabilizing the Expected Outputs of Deep NetworksJoonyoung Yi
The learning of deep models, in which a numerous of parameters are superimposed, is known to be a fairly sensitive process and should be carefully done through a combination of several techniques that can help to stabilize it. We introduce an additional challenge that has never been explicitly studied: the heterogeneity of sparsity at the instance level due to missing values or the innate nature of the input distribution. We confirm experimentally on the widely used benchmark datasets that this variable sparsity problem makes the output statistics of neurons unstable and makes the learning process more difficult by saturating non-linearities. We also provide the analysis of this phenomenon, and based on our analysis, we present a simple technique to prevent this issue, referred to as Sparsity Normalization (SN). Finally, we show that the performance can be significantly improved with SN on certain popular benchmark datasets, or that similar performance can be achieved with lower capacity. Especially focusing on the collaborative filtering problem where the variable sparsity issue has been completely ignored, we achieve new state-of-the-art results on Movielens 100k and 1M datasets, by simply applying Sparsity Normalization (SN).
https://arxiv.org/abs/1906.00150
ON OPTIMIZATION OF MANUFACTURING OF AN AMPLIFIER TO INCREASE DENSITY OF BIPOL...ijoejournal
In this paper we consider a possibility to increase density of bipolar heterotransistor framework an amplifier
due to decreasing of their dimensions. The considered approach based on doping of required areas of
heterostructure with specific configuration by diffusion or ion implantation. The doping finished by optimized
annealing of dopant and/or radiation defects. Analysis of redistribution of dopant with account redistribution
of radiation defects (after implantation of ions of dopant) for optimization of the above annealing
have been done by using recently introduced analytical approach. The approach gives a possibility
to analyze mass and heat transports in a heterostructure without crosslinking of solutions on interfaces
between layers of the heterostructure with account nonlinearity of these transports and variation in time of
their parameters.
Motivation entails the development of a program that automatically performs clustering and outlier detection for a wide variety of numerically represented data.
In this paper we introduce an approach to increase vertical integration of elements of transistor-transistor logic with function AND-NOT. Framework the approach we consider a heterostructure with special configuration. Several specific areas of the heterostructure should be doped by diffusion or ion implantation. Annealing of dopant and/or radiation defects should be optimized.
Matched molecular pair and activity cliffs publishedCresset
In this presentation I present our research into using 3D methods to detect and interpret activity cliffs using Activity Miner. I will show that considering the shape and especially the electrostatic environment around a pair of molecules results in a richer more informed view of the factors causing changes in activity and a hypothesis driven understanding of existing SAR.
ICIC 2014 Knowledge-Based De Novo Molecular Design Using ICSYNTH FRPDr. Haxel Consult
A new knowledge-based approach to the de novo design of synthetically feasible molecules is described. The method is based on specifically designed transform libraries abstracted from reaction databases. The structure generation process is based on conceptual chemistry and the degree of complexity introduced in the new structures can be modulated using specific parameters. Furthermore, this new system allows the integration of the results obtained in different workflows to calculate/predict other important physico-chemical properties of the new suggested molecules.
Lecture: Interatomic Potentials Enabled by Machine LearningDanielSchwalbeKoda
Lecture for the 4th IKZ-FairMAT Winter School. Describes recent advances in neural network interatomic potentials, deep learning models accelerating quantum chemistry, and more.
What is QSAR?, introduction to 3D QSAR, CoMFA, CoMSIA, Case Study on CoMFA contour maps analysis and CoMSIA interactive forces between ligand and receptor, various Statistical techniques involved in QSAR
While Phosphorous (31P) MRS (I) has been promising in experimental and clinical settings since the early 70s, it has been beset by prohibitively lower sensitivity, limited spectral-spatial resolution, and prolonged acquisition. This manuscript and proceedings of the annual scientific meeting of ISMRM in 2022 (REF1) and 2023 (REF2) demonstrate that our novel acquisition strategy, the novel Rosette Trajectory for fast and flexible MR(S)I contrast (Shen et al. 2023 (REF3), later we renamed it as PETALUTE after the translation to the preclinical scanners of 7T and 9.4T), enables operator-independent (1) rapid acquisition (~7 minutes), (2) reconstruction, and (3) processing pipeline, resulting in phosphorous metabolite ratio maps (10 x 10 x 10 mm3) of the whole brain.
In response to the “Repeat it with Me” challenge organized by the Reproducible Research study group of ISMRM, we demonstrated the power of this technique in 5 healthy volunteers at three different institutions with different experimental setups (2nd Place: UTE 31P 3D Rosette MRSI Reproducibility Team, REF4). Since the proposed acquisition/reconstruction/processing pipeline was operator/scanner/coil-independent, the Reproducer sub-teams successfully replicated the findings of the original proceeding in 2022 (REF1). As part of this challenge, we provided some MATLAB scripts and k-space data to reproduce some of the results described in this manuscript. The software and data can be downloaded from https://purr.purdue.edu/projects/ismrm31pmrsi.
These results will likely be of broad interest across clinical settings since the proposed acquisition strategy is not specific to any region, nuclei, or magnetic field and is operator-independent. This study's resolution and signal-to-noise ratios permit the metabolite maps in an experimentally and clinically feasible timeframe at 3 Tesla and 7T.
REF1 Bozymski B, Shen X, Ozen AC, Ibey S, Chiew M, Thomas A, Dydak U, Emir UE. Ultra-Short Echo Time 31P 3D MRSI at 3T with Novel Rosette k-space Trajectory. Proceedings 30th Scientific Meeting, International Society for Magnetic Resonance in Medicine, 2022.
REF2 Farley N, Bozymski B, Dydak U, Emir UE*. Fast 3D 31P MRSI Using Novel Rosette Petal Trajectory at 3T with x4 Accelerated Compressed Sensing. Proceedings 31st Scientific Meeting, International Society for Magnetic Resonance in Medicine, 2023.
REF3 Shen X, Özen AC, Sunjar A, Ilbey S, Sawiak S, Shi R, Chiew M, Emir UE. Ultrashort T2 components imaging of the whole brain using 3D dual-echo UTE MRI with rosette k-space pattern. Magnetic Resonance in Medicine. 2023;89(2):508–521.
REF4 https://challenge.ismrm.org/2023-24-reproducibility-challenge/results-22-23/
2019-06-07 Characterization and research of semiconductors with an FTIR spect...LeonidBovkun
2019-06-07 Educational seminar at EP-3 University of Wuerzburg
I will present particular experiments and related results with FTIR spectrometer, so one may consider these experiments complimentary for you research.
This presentation was given at the 2009 SPIE conference in San Diego, CA.
Actuators employing ferroelectric or ferromagnetic compounds are solid-state, efficient, and compact making them well-suited for aerospace, aeronautic, industrial and military applications. However, they also exhibit frequency, stress and thermally-dependent hysteresis and constitutive nonlinearities which must be incorpo-rated in models for accurate device characterization and control design. A critical step in the use of these models is the estimation or re-estimation of parameters in a manner that is both efficient and robust. In this presentation, we discuss techniques to estimate densities in the homogenized energy model based on Galerkin expansions using physically motivated basis functions. The yields highly tractable optimization algorithms in which initial parameter estimates can be obtained from measured properties of the data. The efficiency and accuracy of the models and estimation algorithms are validated with experimental data.
Matched molecular pair and activity cliffs publishedCresset
In this presentation I present our research into using 3D methods to detect and interpret activity cliffs using Activity Miner. I will show that considering the shape and especially the electrostatic environment around a pair of molecules results in a richer more informed view of the factors causing changes in activity and a hypothesis driven understanding of existing SAR.
ICIC 2014 Knowledge-Based De Novo Molecular Design Using ICSYNTH FRPDr. Haxel Consult
A new knowledge-based approach to the de novo design of synthetically feasible molecules is described. The method is based on specifically designed transform libraries abstracted from reaction databases. The structure generation process is based on conceptual chemistry and the degree of complexity introduced in the new structures can be modulated using specific parameters. Furthermore, this new system allows the integration of the results obtained in different workflows to calculate/predict other important physico-chemical properties of the new suggested molecules.
Lecture: Interatomic Potentials Enabled by Machine LearningDanielSchwalbeKoda
Lecture for the 4th IKZ-FairMAT Winter School. Describes recent advances in neural network interatomic potentials, deep learning models accelerating quantum chemistry, and more.
What is QSAR?, introduction to 3D QSAR, CoMFA, CoMSIA, Case Study on CoMFA contour maps analysis and CoMSIA interactive forces between ligand and receptor, various Statistical techniques involved in QSAR
While Phosphorous (31P) MRS (I) has been promising in experimental and clinical settings since the early 70s, it has been beset by prohibitively lower sensitivity, limited spectral-spatial resolution, and prolonged acquisition. This manuscript and proceedings of the annual scientific meeting of ISMRM in 2022 (REF1) and 2023 (REF2) demonstrate that our novel acquisition strategy, the novel Rosette Trajectory for fast and flexible MR(S)I contrast (Shen et al. 2023 (REF3), later we renamed it as PETALUTE after the translation to the preclinical scanners of 7T and 9.4T), enables operator-independent (1) rapid acquisition (~7 minutes), (2) reconstruction, and (3) processing pipeline, resulting in phosphorous metabolite ratio maps (10 x 10 x 10 mm3) of the whole brain.
In response to the “Repeat it with Me” challenge organized by the Reproducible Research study group of ISMRM, we demonstrated the power of this technique in 5 healthy volunteers at three different institutions with different experimental setups (2nd Place: UTE 31P 3D Rosette MRSI Reproducibility Team, REF4). Since the proposed acquisition/reconstruction/processing pipeline was operator/scanner/coil-independent, the Reproducer sub-teams successfully replicated the findings of the original proceeding in 2022 (REF1). As part of this challenge, we provided some MATLAB scripts and k-space data to reproduce some of the results described in this manuscript. The software and data can be downloaded from https://purr.purdue.edu/projects/ismrm31pmrsi.
These results will likely be of broad interest across clinical settings since the proposed acquisition strategy is not specific to any region, nuclei, or magnetic field and is operator-independent. This study's resolution and signal-to-noise ratios permit the metabolite maps in an experimentally and clinically feasible timeframe at 3 Tesla and 7T.
REF1 Bozymski B, Shen X, Ozen AC, Ibey S, Chiew M, Thomas A, Dydak U, Emir UE. Ultra-Short Echo Time 31P 3D MRSI at 3T with Novel Rosette k-space Trajectory. Proceedings 30th Scientific Meeting, International Society for Magnetic Resonance in Medicine, 2022.
REF2 Farley N, Bozymski B, Dydak U, Emir UE*. Fast 3D 31P MRSI Using Novel Rosette Petal Trajectory at 3T with x4 Accelerated Compressed Sensing. Proceedings 31st Scientific Meeting, International Society for Magnetic Resonance in Medicine, 2023.
REF3 Shen X, Özen AC, Sunjar A, Ilbey S, Sawiak S, Shi R, Chiew M, Emir UE. Ultrashort T2 components imaging of the whole brain using 3D dual-echo UTE MRI with rosette k-space pattern. Magnetic Resonance in Medicine. 2023;89(2):508–521.
REF4 https://challenge.ismrm.org/2023-24-reproducibility-challenge/results-22-23/
2019-06-07 Characterization and research of semiconductors with an FTIR spect...LeonidBovkun
2019-06-07 Educational seminar at EP-3 University of Wuerzburg
I will present particular experiments and related results with FTIR spectrometer, so one may consider these experiments complimentary for you research.
This presentation was given at the 2009 SPIE conference in San Diego, CA.
Actuators employing ferroelectric or ferromagnetic compounds are solid-state, efficient, and compact making them well-suited for aerospace, aeronautic, industrial and military applications. However, they also exhibit frequency, stress and thermally-dependent hysteresis and constitutive nonlinearities which must be incorpo-rated in models for accurate device characterization and control design. A critical step in the use of these models is the estimation or re-estimation of parameters in a manner that is both efficient and robust. In this presentation, we discuss techniques to estimate densities in the homogenized energy model based on Galerkin expansions using physically motivated basis functions. The yields highly tractable optimization algorithms in which initial parameter estimates can be obtained from measured properties of the data. The efficiency and accuracy of the models and estimation algorithms are validated with experimental data.
QSAR STUDY ON READY BIODEGRADABILITY OF CHEMICALS. Presented at the 3rd Chemo...Kamel Mansouri
The goal of this study was to predict ready biodegradation of
chemicals by QSAR modeling. The dataset used for this purpose was
produced by the Japanese Ministry of International Trade and Industry
(MITI) with experimental results according to the OECD test guideline
301C. Molecular descriptors from Dragon 6 were calculated. Variable
selection coupled with classification methods were applied to find the
most predictive models with low cross-validation error rate. The best
models were after that validated using the preselected test set to check
its prediction reliability and for further analysis.
Tim Cheeseright, Assessing the Similarities of Compound collections using mol...Cresset
This presentation, originally given at the 2012 ACS National Meeting in San Diego, investigates alternative methods of defining chemical space using 3D Field based methodologies - the advantages and disadvantages of which are described.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
2. Outline Fields, Field points and the good things you can do with them The alignment problem 3D-QSAR using Fields Examples SARS PLpro – small data set, known xtal structure NK3 – large data set, unknown xtal structure
3. Field Points Condensed representation of electrostatic, hydrophobic and shape properties (“protein’s view”) Molecular Field Extrema (“Field Points”) = Positive = Negative = Shape = Hydrophobic 3D Molecular Electrostatic Potential (MEP) Field Points 2D
4. +ve ionic H-bond acceptor Aromatic p cloud ‘H acceptor’ -ve ionic H-bond donor Hydrophobes Aromatic in-plane ‘H donor’ “Stickiest” surfaces (high vdW) Field points give you new insights into your molecule Explanatory Power of Fields = Positive = Negative = Shape = Hydrophobic Field point sizes show importance
5. Field Points have lots of applications Virtual screening Alignment Pharmacophore elucidation Bioisosteres etc
6. Field Points have lots of applications Virtual screening Alignment Pharmacophore elucidation Bioisosteres etc What about 3D QSAR?
7. The Alignment Problem Historically very difficult Early approaches template-based Issues with side chain orientations Some success with docked data sets Easy to fool yourself Correlation/causation
9. Which is better? “The superior statistical qualities of 3D-QSAR models based on poses that superimpose presumably critical ligand features, rather than docked conformations.” Clark R., JCAMD 2007, p587 Doweyko, J. Comp-Aided Mol. Des., 2004, p 587 Free alignment adds signal, but also noise. Worse statistics, better predictability?
11. N-methyl acetamide Imidazole Field Scoring To score a particular alignment, we use the field points of molecule 1 to sample the actual field of molecule 2 Cheeseright et al, J. Chem Inf. Mod., 2006, 665
12. Field Scoring N-methyl acetamide Imidazole To score a particular alignment, we use the field points of molecule 1 to sample the actual field of molecule 2 and vice-versa Cheeseright et al, J. Chem Inf. Mod., 2006, 665
15. Advantages Many fewer sample points than grid-based methods E.g. Vegfr2 data set Du et al., J Mol Graph Model. 27 (2009) 642-652
16. Advantages Many fewer sample points than grid-based methods Sample points physically rather than statistically chosen Gauge invariant Consistent framework for alignment and QSAR
17. Initial validation Tested against literature CoMFA datasets 15 datasets with alignments available CoMFA average cross-validated RMSE is 0.72 Field QSAR using CoMFA alignments is 0.74 Simple model (volume indicator variable) is 0.83 Data sets re-aligned using field alignment RMSE 1.00
20. The target PLpro (Papain-like protease) is a DUB target which is critical for the replication of the coronavirus responsible for SARS Crystal structures available with bound ligands from 2 series of compounds: structurally related (PDB entries 3E9S and 3MJ5) Small number of analogues – challenge to see if we can use 3D-QSAR for small data sets
24. Summary Able to build a predictive 3D-QSAR model based on small number of analogues Guided (by volume of Xtal structure) alignment worked best. Free alignment was OK, but noisier.
26. NK3 example GPCR target (Tachykinin receptor 3) – selectively binds Neurokinin B – target for treatment of neurological disorders such as schizophrenia Three series of inhibitors from Euroscreen Scaffold-1 – 81 compounds with pIC50 (radioligand binding) in range 4.6-8.7 Scaffold-2 – 80 compounds with pIC50 in range 4.8-7.7 Errors in radioligand binding data c. ± 0.4
27. NK3 binding mode For a 3D method you need a 3D alignment FieldAlign can align to a reference FieldTemplater generates the reference FieldTemplater
28. NK3 binding mode prediction FieldTemplater Selection of 3 highly active scaffold-1 compounds plus 2 structurally dissimilar literature NK3 actives (Talnetant and SB-218795). Generated Templates filtered and candidate selected Conformation of most active scaff-1 structure then used as alignment target for other structures
29. 3D-QSAR details Alignment Free alignment to template conformation Field selection Generated Field points for both steric and electrostatic fields, with both sets at independent locations. 80/20 training/test split Most active and least active training set 2nd most active, 2nd least active test set Random distribution of remaining compounds
30. Initial models problematic When all else fails, talk to the chemists “Are you using the right tautomer?”
33. Extend to scaff-2? Complete lack of predictivity Visual analysis suggests a shift in binding mode for scaff-2 Cross-series QSAR difficult Requires consistent binding modes!
35. Summary Able to generate models based on alignment to predicted active conformation by templating Independent models within each of two series show reasonable predictivity and can be used to guide further work Cross-series analysis suggests different binding modes for the two series
37. Molecular Architect Initially FieldAlign + QSAR Align your molecules Build models Test models Fit new compounds to models Interactive feedback Add additional alignment options
38. Molecular Architect One tool for molecule designers Align QSAR Pharmacophore elucidation Bioisosteres What do I make next? Beta Q4 2011
39. Acknowledgements Cresset Andy Vinter Tim Cheeseright James Melville Chris Earnshaw Euroscreen Hamid Hoveyda JulienParcq