Register for AI Community Newsletter
We are running a very active webinar program that highlights diverse use cases in AI and ML in the biotechnology and pharmaceutical industries.
We welcome suggestions for other topics and speakers too. Please contact Vladimir Makarov (vladimir.makarov at pistoiaalliance dot org)
...
23 May 2018
...
...
- Big Data/ML/DL/AI - fundamentals and concepts
- Data Fidelity
- Real-life use cases in health & life sciences from their book (www.BigDataCXO.com)
- Q & A
...
21 June 2018
...
- Considerations for Life Sciences
- ML 102
- TIE –Interpretability & Explainability
- Conversational AI: Bot Basics
- Q & A
...
1 Oct 2018
...
Drug Repurposing Datathon
- Datathon launch details - Rare disease drug repurposing
- More details & register on Datathon
...
9 Oct 2018
...
Hosting a AI/ML workshop to allow our community to meet, share ideas and make progress on their AI/ML adoption, implementation planning and impact.
Speakers from across the industry and panels, plus networking
...
18 Oct 2018
...
Joint meeting with PRISME forum
Rescheduled from Sep 20
...
Maximizing Value from Healthcare Data Using Machine Learning
- AI maturity model
- CoE update and Datathon
...
10 Dec 2018
...
Webinar panel:
Terry Stouch,
Jamie Powers,
Isabella Fieirberg
Sirarat Sarntivijai
Jabe Wilson
...
26 Feb 2019
...
12 March 2019
...
Register for AI Community Newsletter
We are running a very active webinar program that highlights diverse use cases in AI and ML in the biotechnology and pharmaceutical industries.
We welcome suggestions for other topics and speakers too. Please contact Vladimir Makarov (vladimir.makarov at pistoiaalliance dot org)
| Session | Date | Speakers | Topics & Themes | ||||||
|---|---|---|---|---|---|---|---|---|---|
| 1 | 23 May 2018 | Prashant Natarajan | A Brief History of AI/ML
| ||||||
| 2 | 21 June 2018 | Prashant Natarajan | Demystifying AI – Part 2
| ||||||
| Datathon | 1 Oct 2018 | Datathon launch | Drug Repurposing Datathon
| ||||||
| AI/ML Workshop | 9 Oct 2018 | CoE AI Workshop and network meeting | Hosting a AI/ML workshop to allow our community to meet, share ideas and make progress on their AI/ML adoption, implementation planning and impact. Speakers from across the industry and panels, plus networking | ||||||
| 3 | 18 Oct 2018 | Joint meeting with PRISME forum Rescheduled from Sep 20 | Maximizing Value from Healthcare Data Using Machine Learning
| ||||||
| 4 | 10 Dec 2018 | Webinar panel: Terry Stouch, Jamie Powers, Isabella Fieirberg Sirarat Sarntivijai Jabe Wilson | Matters in data quality: quality scores for data sets and individual data items; FAIR annotations for methods by which data are obtained; Value of old data vs new. Value of even new data on its own and how that can change depending on how it's developed, stored, labeled retrieved, and interpreted. How data and its use can change with age. Different needs of need of the level of quality of the data. How the need for level of quality and variations might differ between methods of analysis. The same data might be considered both junk and useful depending on need; .... PLUS standards for all of the above. | ||||||
| 5 | 26 Feb 2019 | Drs. Alex Tropsha and Ola Engkvist | AI/ML in Drug Design - use neural nets to generate new molecules that are synthetically accessible and fit specified properties. | ||||||
| AI/ML Workshop | 12 March 2019 | CoE AI Workshop and network meeting | Hosting a AI/ML workshop to allow our community to meet, share ideas and make progress on their AI/ML adoption, implementation planning and impact. Speakers from across the industry and panels, plus networking | ||||||
| 6 | 6 June 2019 | Prof. John Overington | Prof. John Overington, the CIO of the Medicines Discovery Catapult described the AssayNet project and its very far reaching implications. | ||||||
| 7 | 20 June 2019 | Webinar Panel:
| Pistoia Alliance and Elsevier Datathon Report Webinar on Drug Repurposing In the late Fall and Winter of 2018, the Pistoia Alliance in cooperation with Elsevier and charitable organizations Cures within Reach and Mission: Cure run a datathon aiming to find drugs suitable for treatment of childhood chronic pancreatitis, a rare disease that causes extreme suffering. The datathon resulted in identification of four candidate compounds in a short time frame of just under three months. In this webinar our speakers will discuss the technologies that made this leap possible. | ||||||
| 8 | 18 September 2019 | Host: Paula Matos (Pistoia Alliance) Webinar Panel:
| Building Trust and Accountability: The Role User Experience Design Can Play in Artificial Intelligence Our panelists described the principles of UX design and its importance in the context of AI, and illustrated with case studies of how UX is being applied in AI. | ||||||
| FAIR, AI and ML workshop | 22 October 2019 | Joint Workshop by the FAIR Implementation Project team and AI/ML CoE | Hosting a AI/ML workshop to allow our community to meet, share ideas and make progress on their AI/ML adoption, implementation planning and impact. Summary of the workshop | ||||||
| 9 | 16 January 2020 | Talk by Dr. Dennis Wang (University of Sheffield) followed by a panel discussion with Mr. Albert Wang (BMS) | Looking beyond the hype: Applied AI and machine learning in translational medicine We will discuss possible ways to enable ML methods to be more powerful for discovery and reduce ambiguity within translational medicine, allowing data-informed decision-making to deliver the next generation of diagnostics and therapeutics to patients quicker, at lowered costs, and at scale. | ||||||
| 10 | 7 April 2020 | Dr. Darren Green, GSK | Automated Molecular Design and the BRADSHAW Platform Dr Darren Green discusses how data-driven chemoinformatics methods may automate much of what has historically been done by a medicinal chemist, considering what the balance is between AI approaches and human expertise and uses examples from Bradshaw, GSK’s experimental automated design environment to support his presentation. | ||||||
| 11 | 4 May 2020 |
| How Can Federated AI/ML Learning Support Genomics and Patient Data Analysis to Enable Precision Medicine at Scale? Organized by the Digital Medicine Program at the Pistoia Alliance and the Digital Medicine (DiMe) Society How federated learning can help overcome some of the barriers seen in the development of AI-based solutions for pharma, genomics and healthcare? Following the presentation, the panel debate on other elements that could drive the adoption of digital approaches more widely and help answer currently intractable science and business questions. | ||||||
| 12 | 26 June 2020 |
| Putting AI into Practice Is it possible to forecast which of the drug discovery projects would advance to clinical trials? A talk on "Mining Drug-Target-Disease Trends from Public Data Sources" presented by Andrew Prigodich, and Peter V Henstock from Pfizer and a Harvard University Extension School team (Andrew Wang, Bhavani Shekhawat, Charlie Flanagan, Derek Kinzo, Gerald Ding, Ramandeep Hariai, Roman Burdakov), followed by a panel discussion by James Weatherall, AstraZeneca, John Overington, Medicines Discovery Catapult, and Peter Henstock, Pfizer. | ||||||
| 13 | 9 December 2020 |
| Minimal Information Standard for an AI Model Artificial intelligence and machine learning models are used more and more often in the development of pharmaceuticals and as software components in medical devices. However, because there has been a lack of clear reporting standards, many clinically relevant models have been reported with insufficient details to properly assess their risks and benefits. Historically, this has made the science underlying these products irreproducible, deployment and comparison of AI algorithmic solutions hard, and may lead to the users of these products facing unequal or unforeseen harms. Therefore a standard for reporting of biomedically-relevant AI/ML models is necessary. In this panel discussion we will brainstorm options for the transparent reporting of AI algorithms in biology and medicine. Participants include Prof. Atul Butte and Dr. Beau Norgot, authors of the MI-CLAIM checklist recently published in Nature, and Drs. Jen Harrow, Fotis Psomopoulos, and Tom Lenaerts, who are actively working on the standards for AI and ML in Europe. | ||||||
| 14 | 20 January 2021 |
| AI for Drug Repurposing Chemical-induced gene expression profiles provide a mechanistic signature of phenotypic response, and are thus promising for drug repurposing. However, the use of such data is limited by their sparseness, unreliability, and relatively low throughput. Our speakers, Drs. Aleksandar Poleksic and Lei Xie, describe two new computational techniques for prediction of the differential gene expression profiles perturbed by de novo chemicals and inference of drug-disease associations. | ||||||
| 15 | 27 January 2021 | Panel discussion:
| Imaging Biomarkers Biomarkers have become an essential part of the drug discovery and development process. A biomarker-driven approach to developing targeted therapies and patient selection strategies has the potential to increase success in the drug development process, decrease costs, and ultimately improve patient outcomes. But what about imaging biomarkers? Usually obtained from PET, MRI, and CT scans, they comprise measurements of structural and metabolic features of the body that over time are used to assess disease progression and response to treatment. Imaging biomarkers are an ideal method to draw evidence from retrospective data and can be used both as inclusion criteria—to select relevant cohorts of patients and output data—to quantify responses to treatments.
| ||||||
| 16 | 15 February 2021 | Prashant Natarajan, Vice President of AI & Analytics Solutions, H2O.ai | Real-World Evidence - Levering AI and Analytics For Real Value and Lasting Impact Real-world evidence is not new, but with advances in processes, technology, policy, and analytics, is becoming more accessible and usable. RWE is being used to drive real outcomes and lasting impact for pharma, patients/subjects, and other participants in the continuum of care. At the foundation of RWE is data – behaviors, patterns, computational biomarkers, phenotypic/genomic data, imaging, outcomes, and social determinants of health. The RWE trends that are happening in life sciences and biological sciences are driven by
While data and descriptive analytics have been in vogue for years, advances in processing RWE – in combination with RCTs via data science, machine/deep learning, and advanced analytics – are creating new value for Pharma companies across the board – not just in R&D and pharmacovigilance but also extending into economic value, sales & marketing, affordable therapies, and patient outcomes. More importantly, with the success of these analytics and AI efforts, we will see an increasing appetite for more types of RWE – beyond EMRs, all-claims, and commercial data sets – into patient-reported experiences, wearables, at-home devices, and implants. Creating value at scale and achieving lasting impact is important, doable, and repeatable. This presentation will provide practical recommendations on how to put this tsunami of RWE and data variety to work using the IMPACT framework. We will conclude with a discussion of representative use cases that pharma and biotechnology organizations can use to move the needle from a product focus to customized/personalized therapies, precision medicine, and population health. Speaker: Prashant Natarajan, Vice President of AI & Analytics Solutions, H2O.ai and Pistoia Alliance AI CoE Advisory Committee Member Please note: This presentation was originally delivered during the Qiagen Digital Insights hackathon in February 2021 and is being shared with permission. All rights reserved. | ||||||
| 17 | 25 March 2021 | The Pistoia Alliance DataFAIRy Team:
| Lessons Learned in a Pilot BioAssay Annotation Project In 2020, a team of scientists from AstraZeneca, Bristol Myers Squibb, Novartis, and Roche set forth to find a way to convert unstructured biological assay descriptions into FAIR information objects. In this talk, we will present the lessons learned in the pilot project to annotate bioassay descriptions (bioassay) en masse and will chart a way to expand this effort in the future. | ||||||
| 18 | 21 April 2021 There are 2 sessions: 21 April at 8-9 am PST, and a repeat for the APAC time zones on 22nd April 4-6:30 pm | Dr. Vladimir Makarov, PhD, MBA, Pistoia Alliance | Pistoia Alliance AI CoE, FAIR, and DataFAIRy Invited short talk at the Research Data Alliance Virtual Plenary "FAIR 4 ML"; see full agenda and the direct link to our session "Defining FAIR for AI" | ||||||
| 19 | 21 April 2021 Talk starts at 12:20 pm EDT (9:20 am PST) |
| Panel Discussion: The Pistoia Alliance DataFAIRy Project Part of the Pistoia Alliance Conference - Collaborative R&D in Action | ||||||
| 20 | 5 May 2021 | Panelists:
| Technical strategies against bias in AI There is an increasing number of reports discussing the urgent need for addressing bias in decision making algorithms in healthcare. In fact, a recent JAMA commentary published in 2021 (link) highlighted systemic kidney transplantation inequities for black individuals. With AI-based and machine learning techniques increasingly playing a role in healthcare decision making, it becomes necessary to discuss not only the ethical implications but solutions and approaches to detect and reduce the impact of computer bias in healthcare. The Pistoia Alliance is happy to announce the "Technical strategies against bias in AI", which will bring together industry experts to share lessons learned and discuss possible solutions. | ||||||
| 21 | 2 June 2021 8 am PST = 11 am EST = 4 pm London |
| DataFAIRy Bioassay Annotation Invited short talk at the Cambridge Cheminformatics meeting | ||||||
| 22 | 2 June 2021 8 am PST = 11 am EST = 4 pm London |
| Optimizing Kinase Profiling Programs with Deep Learning Join Genentech and Optibrium for this discussion of Alchemite™, a novel deep learning approach, and its application to optimizing kinase profiling programs. Using Alchemite™ reduces the number of kinase assays required to accurately predict the full kinase selectivity profile, effectively accelerating experimental programs. The team will demonstrate the method’s performance on a data set of approximately 650 kinases and 10,000 compounds, significantly outperforming state-of-the-art quantitative structure-activity relationship (QSAR) approaches, including multi-target deep learning. Furthermore, we will discuss Alchemite’s unique ability to provide reliable prediction-uncertainty-estimates that enable the selection of the most informative kinase assays and which compounds to test. | ||||||
| 23 | 30 June 2021 8 am PST = 11 am EST = 4 pm London |
| Building the future of collaborative research with federated learning Federated learning is a new machine-learning paradigm where multiple partners can collaborate on complex research questions without centralising or sharing data outside of their organizations. This ‘collaborative machine learning’ approach enables data science teams to work on larger and more diverse datasets, previously inaccessible, boosting the predictive power of machine learning algorithms and enhancing AI capabilities. By overcoming privacy and confidentiality concerns, companies can build partnerships and consortia and retain their competitive edge. For example, the MELLODDY consortium pioneers federated learning-based drug discovery across 10 pharma companies benefiting from the collective insights of the world’s largest cheminformatics data network where each participant retains full confidentiality and governance over their molecular libraries.
| ||||||
| 24 | 28 July |
| Optimizing Kinase Profiling Programs with Deep Learning Join Genentech and Optibrium for this discussion of Alchemite™, a novel deep learning approach, and its application to optimizing kinase profiling programs. Using Alchemite™ reduces the number of kinase assays required to accurately predict the full kinase selectivity profile, effectively accelerating experimental programs. The team will demonstrate the method’s performance on a data set of approximately 650 kinases and 10,000 compounds, significantly outperforming state-of-the-art quantitative structure-activity relationship (QSAR) approaches, including multi-target deep learning. Furthermore, we will discuss Alchemite’s unique ability to provide reliable prediction-uncertainty-estimates that enable the selection of the most informative kinase assays and which compounds to test. | ||||||
| 23 | 30 June 2021 8 am PST = 11 am EST = 4 pm London |
| Building the future of collaborative research with federated learning Federated learning is a new machine-learning paradigm where multiple partners can collaborate on complex research questions without centralising or sharing data outside of their organizations. This ‘collaborative machine learning’ approach enables data science teams to work on larger and more diverse datasets, previously inaccessible, boosting the predictive power of machine learning algorithms and enhancing AI capabilities. By overcoming privacy and confidentiality concerns, companies can build partnerships and consortia and retain their competitive edge. For example, the MELLODDY consortium pioneers federated learning-based drug discovery across 10 pharma companies benefiting from the collective insights of the world’s largest cheminformatics data network where each participant retains full confidentiality and governance over their molecular libraries.
| ||||||
| 24 | 28 July 2021 8 am PST = 11 am EST = 4 pm London |
| Challenges in the regulation of AI Software as a Medical Device Software as a medical device (SaMD) that leverages artificial intelligence (AI) has the opportunity to reshape healthcare. It also raises unique challenges for developers and regulators. As healthcare advances and digital solutions leveraging AI become more prevalent, it is important that medical device regulatory frameworks also advance to match the speed of innovation. The panel will review key terms related to AI SaMD and describe unique regulatory challenges associated with devices that leverage AI. Additionally, the panel will explore novel regulatory approaches to the regulation of AI SaMD currently under consideration by international regulatory authorities. | ||||||
| 25 | 8 September 2021 8 am PST = 11 am EST = 4 pm London | Andreas Bender, Reader for Molecular Informatics at Cambridge University, and Director Digital Life Sciences at Nuvisan/ICB | Artificial Intelligence in Drug Discovery – What is Realistic, What are Illusions? Although artificial intelligence (AI) has had a profound impact on areas such as image recognition, comparable advances in drug discovery are rare. We will discuss the stages of drug discovery in which improvements in the time taken, success rate or affordability will have the most profound overall impact on bringing new drugs to market. Changes in clinical success rates will have the most profound impact on improving success in drug discovery; in other words, the quality of decisions regarding which compound to take forward (and how to conduct clinical trials) are more important than speed or cost. Although current advances in AI focus on how to make a given compound, the question of which compound to make, using clinical efficacy and safety-related end points, has received significantly less attention. As a consequence, current proxy measures and available data cannot fully utilize the potential of AI in drug discovery, in particular when it comes to drug efficacy and safety in vivo. Thus, addressing the questions of which data to generate and which end points to model will be key to improving clinically relevant decision-making in the future. | ||||||
| 26 | 4 November 2021 8 am PST = 11 am EST = 4 pm London |
| Challenges in the regulation of AI Software as a Medical Device Software as a medical device (SaMD) that leverages artificial intelligence (AI) has the opportunity to reshape healthcare. It also raises unique challenges for developers and regulators. As healthcare advances and digital solutions leveraging AI become more prevalent, it is important that medical device regulatory frameworks also advance to match the speed of innovation. The panel will review key terms related to AI SaMD and describe unique regulatory challenges associated with devices that leverage AI. Additionally, the panel will explore novel regulatory approaches to the regulation of AI SaMD currently under consideration by international regulatory authorities. | 25 | 8 September 2021Jacob Aptekar, MD, PhD, Senior Director of Product Management, Acorn AI, a part of Dassault Systems | How Synthetic Data Is Unlocking a Decade's Worth of Clinical Trial Data to Power a New Era of Drug Development Historic clinical trial data (HCT) is emerging as an important source of evidence across clinical development. Data from past trials is often superior to real-world data from EMR records etc. as it is more structured, complete, 100% traceable and contains the typical endpoints and covariates captured in a clinical trial. Regulators have lately been supportive of the use of HCT data with both the FDA and EMA approving hybrid trials: phase 3 trials where patients from the control arm have been replaced by synthetic patients from past trials. This talk will explore methodologies and use cases for Synthetic Patients - 'digital twins' of real patients that replicate their behavior to a very high degree. Synthetic Patients enable easy sharing of patient-level data without risk of subject-level or sponsor disclosure while allowing data scientists to mine deep insights on patient characteristics and behavior. | |||
| 27 | 23 February 2022 8 am PST = 11 am EST = 4 pm London | Andreas Bender, Reader for Molecular Informatics at Cambridge University, and Director Digital Life Sciences at Nuvisan/ICB | Artificial Intelligence in Drug Discovery – What is Realistic, What are Illusions? Although artificial intelligence (AI) has had a profound impact on areas such as image recognition, comparable advances in drug discovery are rare. We will discuss the stages of drug discovery in which improvements in the time taken, success rate or affordability will have the most profound overall impact on bringing new drugs to market. Changes in clinical success rates will have the most profound impact on improving success in drug discovery; in other words, the quality of decisions regarding which compound to take forward (and how to conduct clinical trials) are more important than speed or cost. Although current advances in AI focus on how to make a given compound, the question of which compound to make, using clinical efficacy and safety-related end points, has received significantly less attention. As a consequence, current proxy measures and available data cannot fully utilize the potential of AI in drug discovery, in particular when it comes to drug efficacy and safety in vivo. Thus, addressing the questions of which data to generate and which end points to model will be key to improving clinically relevant decision-making in the future. | 26 | Jacob Aptekar, MD, PhD, Senior Director of Product Management, Acorn AI, a part of Dassault Systems | How Synthetic Data Is Unlocking a Decade's Worth of Clinical Trial Data to Power a New Era of Drug Development Historic clinical trial data (HCT) is emerging as an important source of evidence across clinical development. Data from past trials is often superior to real-world data from EMR records etc. as it is more structured, complete, 100% traceable and contains the typical endpoints and covariates captured in a clinical trial. Regulators have lately been supportive of the use of HCT data with both the FDA and EMA approving hybrid trials: phase 3 trials where patients from the control arm have been replaced by synthetic patients from past trials. This talk will explore methodologies and use cases for Synthetic Patients - 'digital twins' of real patients that replicate their behavior to a very high degree. Synthetic Patients enable easy sharing of patient-level data without risk of subject-level or sponsor disclosure while allowing data scientists to mine deep insights on patient characteristics and behavior. | 27 | 23 February Martin-Immanuel Bittner, MD, DPhil, FRSA, the Chief Executive Officer and co-founder of Arctoris | Combining Robotics and Machine Learning for Accelerated Drug Discovery Artificial intelligence has an increasing impact on drug discovery and development, offering opportunities to identify novel targets, hit, and lead-like compounds in accelerated timeframes. However, the success of any AI/ ML model depends on the quality of the input data, and the speed with which in silico predictions can be validated in vitro. The talk will cover laboratory automation and robotics and the benefits they offer in terms of quality and speed of data generation synergized with AI/ ML-powered drug discovery approaches. The talk will cover some of the general trends in the industry, and also highlight successfully implemented case studies that show how the combination of robotics and AI/ ML lead to accelerated project timelines and superior research outputs. |
| 28 | 12 May 2022 8 am PST = 11 am EST = 4 pm London | Karl Leswing, Machine Learning Tech Lead, Schrödinger | AI/ML Webinar: AI Tools for Drug Design - AutoDesigner, a De Novo Design Algorithm The lead optimization stage of a drug discovery program generally involves the design, synthesis, and assaying of hundreds to thousands of compounds. The design phase is usually carried out via traditional medicinal chemistry approaches and/or structure-based drug design (SBDD) when suitable structural information is available. Two of the major limitations of this approach are (1) difficulty in rapidly designing potent molecules that adhere to myriad project criteria, or the multiparameter optimization (MPO) problem, and (2) the relatively small number of molecules explored compared to the vast size of chemical space. To address these limitations we have developed AutoDesigner, a de novo design algorithm. | ||||||
| 29 | 29 June 2022 8 am PST = 11 am EST = 4 pm London |
| Valuation of AI Technology Investments Today the hype that surrounded artificial intelligence in the previous years is largely gone, and the industry practitioners are looking for solid use cases and proof of value. Prashant Natarajan, VP of H2O.ai, and Dr. Peter Henstock, ML&AI Technical Lead, Pfizer, will discuss the methods for valuation of AI investments in the pharmaceutical industry. | ||||||
| 30 | 3 August 2022 8 am PST = 11 am EST = 4 pm London | Martin-Immanuel Bittner, MD, DPhil, FRSA, the Chief Executive Officer and co-founder of Arctoris | Combining Robotics and Machine Learning for Accelerated Drug Discovery Artificial intelligence has an increasing impact on drug discovery and development, offering opportunities to identify novel targets, hit, and lead-like compounds in accelerated timeframes. However, the success of any AI/ ML model depends on the quality of the input data, and the speed with which in silico predictions can be validated in vitro. The talk will cover laboratory automation and robotics and the benefits they offer in terms of quality and speed of data generation synergized with AI/ ML-powered drug discovery approaches. The talk will cover some of the general trends in the industry, and also highlight successfully implemented case studies that show how the combination of robotics and AI/ ML lead to accelerated project timelines and superior research outputs. | 28 | 12 May | Trustworthy AI Our speakers, Navdeep Gill (H2O.ai) and Chas Nelson (gliff.ai) will present a perspective on trustworthy and responsible AI. We will discuss various components that contribute to responsible AI and the new ANSI standard “ANSI/CTA 2090 Use of Artificial Intelligence in Health Care: Trustworthiness” and the ways to implement trustworthy and responsible AI in practice covering the whole artificial intelligence lifecycle. | |||
| 31 | 19 October 2022 8 am PST = 11 am EST = 4 pm London | Karl Leswing, Machine Learning Tech Lead, Schrödinger | AI/ML Webinar: AI Tools for Drug Design - AutoDesigner, a De Novo Design Algorithm The lead optimization stage of a drug discovery program generally involves the design, synthesis, and assaying of hundreds to thousands of compounds. The design phase is usually carried out via traditional medicinal chemistry approaches and/or structure-based drug design (SBDD) when suitable structural information is available. Two of the major limitations of this approach are (1) difficulty in rapidly designing potent molecules that adhere to myriad project criteria, or the multiparameter optimization (MPO) problem, and (2) the relatively small number of molecules explored compared to the vast size of chemical space. To address these limitations we have developed AutoDesigner, a de novo design algorithm. | 29 |
| Valuation of AI Technology Investments Today the hype that surrounded artificial intelligence in the previous years is largely gone, and the industry practitioners are looking for solid use cases and proof of value. Prashant Natarajan, VP of H2O.ai, and Dr. Peter Henstock, ML&AI Technical Lead, Pfizer, will discuss the methods for valuation of AI investments in the pharmaceutical industry. | 30 | 3 August 2022
| Generative Therapeutics Design: Accelerating Drug Discovery with AI and Machine Learning The application of Artificial Intelligence/Machine Learning (AI/ML) methods in drug discovery are maturing and their utility and impact is likely to permeate many aspects of drug discovery. Numerous methods, however, utilize structure-activity relationship (SAR) data without explicit use of 3D structural information of the ligand protein complex. Gilead is using BIOVIA’s Generative Therapeutics Design (GTD) method to take advantage of 3D structural models, i.e. pharmacophoric representation of ligand protein interaction as well as typical docking/scoring steps. Using Gilead’s SAR data set pertaining to the discovery of spleen tyrosine kinase (SYK) inhibitors Entospletinib and Lanraplenib they found that common types of problems in medicinal chemistry can be effectively addressed via GTD. |
| 32 | 16 February 2023 8 am PST = 11 am EST = 4 pm London | Trustworthy AI Our speakers, Navdeep Gill (H2O.ai) and Chas Nelson (gliff.ai) will present a perspective on trustworthy and responsible AI. We will discuss various components that contribute to responsible AI and the new ANSI standard “ANSI/CTA 2090 Use of Artificial Intelligence in Health Care: Trustworthiness” and the ways to implement trustworthy and responsible AI in practice covering the whole artificial intelligence lifecycle. | |||||||
| 31 | 19 October 2022 8 am PST = 11 am EST = 4 pm London |
| Generative Therapeutics Design: Accelerating Drug Discovery with AI and Machine Learning The application of Artificial Intelligence/Machine Learning (AI/ML) methods in drug discovery are maturing and their utility and impact is likely to permeate many aspects of drug discovery. Numerous methods, however, utilize structure-activity relationship (SAR) data without explicit use of 3D structural information of the ligand protein complex. Gilead is using BIOVIA’s Generative Therapeutics Design (GTD) method to take advantage of 3D structural models, i.e. pharmacophoric representation of ligand protein interaction as well as typical docking/scoring steps. Using Gilead’s SAR data set pertaining to the discovery of spleen tyrosine kinase (SYK) inhibitors Entospletinib and Lanraplenib they found that common types of problems in medicinal chemistry can be effectively addressed via GTD. | ||||||
| 32 | 16 February
| FAIR Assay Annotation Project by the Pistoia Alliance and the NIH This is a webinar for the NIEHS audience to inform the NIH about the progress in the Pistoia Alliance FAIR Assay Annotation project (a.k.a. "DataFAIRy") and to brainstorm the future collaboration opportunities between the Pistoia Alliance and the NIH. The event itself is limited to the NIH participants and the PA speakers, but the recording may be made publicly available, subject to agreement with the NIEHS. | |||||||
| 33 | 29 March 2023 7 am PST = 10 am EST = 3 pm London Future Event |
| How Important is Subject Matter Expertise in Life Sciences When Using Technology and Artificial Intelligence? Sponsored Event With recent developments in technology, and the accessibility of artificial intelligence models, one must consider the importance of subject matter expertise in ensuring these are used in the most applicable and accurate settings. Further highlighted during a recent well-documented chatbot unveiling, even incredibly well funded efforts can provide factual errors that will only be spotted by such experts. This expert input is even more important in the highly ambiguous, synonymous and complex domain of life sciences. Here, we cover the importance of such expertise in the development, fine-tuning as well as application of, technologies, including artificial intelligence, in the life sciences – also touching on how these can impact end users. | ||||||
| 34 | 5 April 2023 8 am PST = 11 am EST = 4 pm London Future Event |
| FAIR Assay Annotation Project by the Pistoia Alliance and the NIH This is a webinar for the NIEHS audience to inform the NIH about the progress in the Pistoia Alliance FAIR Assay Annotation project (a.k.a. "DataFAIRy") and to brainstorm the future collaboration opportunities between the Pistoia Alliance and the NIH. The event itself is limited to the NIH participants and the PA speakers, but the recording may be made publicly available, subject to agreement with the NIEHS. | 33 |
| Good Machine Learning Practices by the Pistoia Alliance AI CoI The PA GMLP Team Starting in 2021, a team of Pistoia Alliance colleagues conducted in-depth business analysis centered on the use of AI in the pharmaceutical enterprise, and identified common use cases, challenges, and best practices for application of AI, specific to particular personas. This webinar presents the interim report of the results to-date, followed by the panel discussion by the members of the Pistoia Alliance GMLP CoI. | |||
| 35 | Planning |
| Use Cases in Government Regulation of AI/ML and Compliance E.g.: | ||||||
| 34 | Planning | The PA GMLP Team | Good Machine Learning Practices (GMLP) work at the Pistoia Alliance A summary of our GMLP efforts for the Pistoia Alliance community. | ||||||
| 3536 | Planning | The PA DataFAIRy Team | FAIR Assay Annotation Project by the Pistoia Alliance A report for the Pistoia Alliance community of the work in the DataFAIRy project in 2022 and 2023, and the synergies with other projects that impact the research data quality (IVP, SEED), to be delivered in the second half of 2023. | ||||||
| Idea | TBD | Exscientia, i.e. John Overington | Fast and accurate generative AI design of novel antibodies | ||||||
| Idea | TBD | TBD | Use of Predicted Protein Structure in AI-driven CADD RE: https://pubs.rsc.org/en/content/articlelanding/2023/SC/D2SC05709C | ||||||
| Idea | TBD | Seeking input from UC Engineering and contacts at the OpenAI | Large Language Models Such as ChatGPT and other NLG efforts | ||||||
| Idea | TBD | TBD | Knowledge Graphs in Pharmaceutical Discovery Include KG companies: Wisecube, Elsevier, Biorelate, BenchSci | ||||||
| Idea | TBD | Mika Lindvall, Novartis Institutes for BioMedical Research, Emeryville, California 94608 Email: mika.lindvall@outlook.com | RE: the paper: An Artificial Intelligence Approach to Proactively Inspire Drug Discovery with Recommendations |
...