Presentations with fellows at conferences, workshops and meetings

2024

• Alan Kai Hassen gave a talk based on the article "Generate what you can make: achieving in-house synthesizability with readily available resources in de novo drug design" (23.10.2024).
• Vincenzo Palmacci gave a talk titled "Statistical approach enabling technology-specific assay interference prediction from large screening data sets". (30.04.2024)
• Mikhail Andronov gave a talk based on his recently uploaded pre-print, titled "A reagent-driven visual method for analyzing chemical reaction data" (21.02.24)
• Julian Cremer presented two posters "Multi-Objective Guidance via Importance Sampling for Target-Aware Diffusion-based De Novo Ligand Generation" and "Latent-Guided Equivariant Diffusion for Controlled Structure-Based De Novo Ligand Generation" at the ML4MLS Workshop at ICML 2024 (July 26th, 2024)
• Vincenzo Palmacci gave a talk titled "Statistical approach enabling technology-specific assay interference prediction from large screening data sets". (April 30th, 2024)
• Emma Svensson gave a talk "HyperPCM: Robust Task-Conditioned Modeling of Drug-Target Interactions" for the M2D2 talk series from Valence Labs at Mila (April 2nd, 2024). Watch the talk here!
• All fellows presented posters and gave flash talks at the final AIDD School in Berlin (March 7th and March 12th respectively).
• Mikhail Andronov gave a talk based on his recently uploaded pre-print, titled "A reagent-driven visual method for analyzing chemical reaction data" (February 21st)

2023

• Yasmine Nahal presented a poster "Leveraging expert feedback to align proxy and ground truth rewards in goal-oriented molecular generation" at the AI4D3 Workshop at NeurIPS 2023 (December 15, New Orleans, USA)
• All fellows also presented their posters during the AIDD School in AALTO (March 21, Espoo/Helsinki, Finland)
• Julian Cremer gave an online lecture  titled "Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation" (December 12th 2023)
• Arslan Masood gave an online lecture titled "Dissecting Drug-Induced-Liver-Injury" (November 28th 2023)
• Friesacher, R gave an online lecture, titled "Understanding Model Uncertainty and Enhancing Probability Calibration in Neural Networks" (October 25th 2023)
• Voinarovska, V.  gave an online lecture titled “When yield prediction does not yield prediction: an overview of the current challenges” (September 19th, 2023)
• Andronov, M. gave a talk "Reagent prediction with a molecular transformer improves reaction data quality" at ACS meeting in San Francisco (August 13th, 2023)
• Torren-Peraire, P. gave a talk "Models Matter: The Impact of Single-Step Retrosynthesis on Synthesis Planning" at ACS meeting in San Francisco (August 13th, 2023)
• Radaeva, M. "Targeting Androgen Receptor with CADD" (July 19th, 2023)
• Presentation of OCHEM model from Kaggle 1st EUOS/SLAS Joint Challenge: Compound Solubility Challenge at during HMGU MTCC workshop (July 7th, 2023)
• Paula Torren-Peraire: Enhancing Chemical Synthesis Planning through Combining Single-Step and Multi-Step Retrosynthesis Prediction Strategies. STB seminar (departmental seminar). (May 26th, 2023).
• Mikhail Andronov gave a talk about reagent prediction at the DigiDrug seminar in Berlin organized by Prof. Andreas Bender (May 24th, 2023).
• Invited lecture to present winning model of the Kaggle 1st EUOS/SLAS Joint Challenge: Compound Solubility Challenge at SLAS 2023 conference in Brussels (Peter Hartog  is a team member)
• Torren-Peraire, P. AI in the Lab: How Machine Learning Can Transform Chemical Synthesis. Pint of Science Munich. (May 22nd, 2023).
• Ha, S.V.  FSL-CP: Few-shot Prediction of small molecule activity using cell microscopy images. AIDD on-line seminar  (May 17th, 2023)
• Masood, A., Heinonen, M., Herman, D., Ceulemans, H. Kaski, S. Dos-Time dependent DILI modeling.  In Janssen Discovery Data Science meeting. (May 8th, 2023).
• Nahal, Y. Heinonen, M., Engkvist, O. Kaski, S. Human-in-the-loop active learning to improve molecular design and optimization. In AstraZeneca Molecular Design meetings. (April 13th, 2023).
• Torren-Peraire P. Mind the Retrosynthesis Gap: Bridging the divide between Single-step and Multi-step Retrosynthesis Prediction. AIDD on-line seminar (April 5th, 2023)
• Hartog, P., Genheden, S., Tetko, I. Two sides of the same coin: The effect of smiles-based molecular representations on explainability. <Interact> Conference (March 31, 2023)
• Hassen AK., Torren-Peraire P., Genheden S., Verhoeven J, Preuss M., Tetko I. Mind the Retrosynthesis Gap: Bridging the divide between Single-step and Multi-step Retrosynthesis Prediction. <Interact> Conference. (March 30th, 2023). 
• Fallani, A. Extending 3D generative modeling of molecules with quantum-mechanical properties. AIDD on-line seminar  (March 8, 2023)
• Cremer, J. Equivariant Graph Neural Networks for Toxicity Prediction.  AIDD on-line seminar (February 1st, 2023)
• Andronov, M. Reagent prediction with a transformer and its benefits for reaction product prediction. AIDD on-line seminar (February 1st, 2023)

2022

All fellows also presented their posters during the AIDD School in KUL (October 20, Leuven, Belgium)

• Palmacci V. Drug Discovery and Cheminformatics: discovering new drugs in the Big Data era. Seminar at University of Boogna. December 02, 2022.
• Hassen AK., Torren-Peraire P., Genheden S., Verhoeven J, Preuss M., Tetko I. Mind the Retrosynthesis Gap: Bridging the divide between Single-step and Multi-step Retrosynthesis Prediction. NeurIPS 2022 workshop AI for Science: Progress and Promises. December 2, 2022.
• Svensson, E., Hoedt, P.-J., Hochreiter, S., Klambauer, G. Robust task-specific adaption of models for drug-target interaction prediction. In NeurIPS2022 AI4Science Workshop. November 28, 2022.
• Svensson, E., Hoedt, P.-J., Hochreiter, S., Klambauer, G. Task-conditioned modeling of drug-target interactions. In ELLIS Machine Learning for Molecules Discovery Workshop. November 28, 2022.
• Svensson, E., Hoedt, P.-J., Hochreiter, S., Klambauer, G. Task-conditioned modeling of drug-target interactions. In NeurIPS2022 Women in Machine Learning Workshop. November 28, 2022.
• Sanchez-Fernandez, A.; Rumetshofer, E.; Hochreiter, S.; Klambauer, G. Cross-modal Contrastive Learning of Microscopy Image- and Srtructure-Based Representations of Molecules. In NeurIPS2022 Women in Machine Learning Workshop. November 28, 2022. 
• Ha, S.V. , Tandon A., Czodrowski, P. Overview of Czodrowski Lab AK-Symposium. Johannes Gutenberg University Mainz. November 10th, 2022.
• Masood, A., Heinonen, M., Herman, D., Ceulemans, H. Kaski, S. Dos-Time dependent DILI modeling.  In STB, Helmholtz Zentrum München. November 08, 2022.
• Ha, S.V. Few-shot bioassay prediction with Cell Painting for drug discovery. RdKit UGM 2022. October 13th, 2022.
• Sanchez-Fernandez, A. Cross-Modal Contrastive Learning of Microscopy Image and Structure-Based Representations of Molecules. CytoData Symposium. October 10, 2022.
• Masood, A., Heinonen, M., Herman, D., Ceulemans, H. Kaski, S. Dos-Time dependent DILI modeling.  In Finnish Center of Artificial Intelligence Virtual Drug Design Lab seminars. October 10, 2022.
• Andronov, M.; Voinarovska, V.; Wand, M.; Schmidhuber, J. Reagent Prediction With a Molecular Transformer Improves Reaction Data Quality. 23rd EuroQSAR, Heidelberg, Germany, September 2022
• Voinarovska, V.; Dudenko, D.; Torren-Peraire, P.; Tetko, I.; Genheden, S. Addressing the applicability domain in yield prediction, 23rd EuroQSAR, Heidelberg, September 26-30, Germany 2022,
• Nahal, Y. Heinonen, M., Engkvist, O. Kaski, S. Human-in-the-loop active learning to improve molecular design and optimization. In Finnish Center of Artificial Intelligence Virtual Drug Design Lab seminars. September 6, 2022.
• Friesacher H.R., Lewis Mervin L., Engkvist O., Moreau Y., Arany A. Can we trust probabilities in deep drug activity models? A comparative calibration study. 21st European Conference on Computational Biology. September 15, 2022.
• Sanchez-Fernandez, A., Rumetshofer, E., Hochreiter, S., and Klambauer, G. Contrastive learning of image-and structure-based representations in drug discovery. In ICML2022 Workshop on Women in Machine Learning. July 18th, 2022
• Radaeva, M. "Novel CADD-designed Lin28B Inhibitors Suppress Stemness in Neuroendocrine Prostate Cancer" in Strasbourg Chemoinformatics Conference, July 1st, 2022
• Fallani, A.,  Medrano Sandonas, L. and Tkatchenko, A. Towards the inverse design of molecules with target quantum mechanical properties. In University of Luxembourg Department Workshop. June 2022.
• Svensson, E.,  Hartog, P. AIDD Codebase: a Framework for Model Integration, Collaboration and Sharing. AIDD on-line seminar (June 22, 2022)
• Svensson, E., Hoedt, P.-J., Hochreiter, S., Klambauer, G.  Robust task-specific adaption of drug-target interaction models. In ICML2022 Workshop on Women in Machine Learning. July 18th, 2022.
• Sanchez-Fernandez, A.,  Contrastive Learning of Image and Structure-Based Representations in Drug Discovery.  AIDD on-line seminar (June 8th, 2022)
• Sanchez-Fernandez, A., Rumetshofer, E., Hochreiter, S., and Klambauer, G. Contrastive learning of image-and structure-based representations in drug discovery. In ICLR2022 Workshop on Machine Learning for Drug Discovery. (April 29th, 2022)
• Fallani, A.,  Medrano Sandonas, L. and Tkatchenko, A. Towards the inverse design of molecules with target quantum mechanical properties. In APS March Meeting. March 2022.
• Nahal, Y.  A Survey on Human-in-the-loop Machine Learning on-line AIDD lecture (March 16, 2022)
• Andronov, M. Overview of the methods for chemical reaction prediction,  Rising Stars in AI Symposium 2022 in KAUST, Thuwal, Saudi Arabia (March 13, 2022)
• Nahal, Y. Learning from user feedback to improve recommender models and potential applications to molecular design. In Finnish Center of Artificial Intelligence Virtual Drug Design Lab seminars. (March 1st, 2022)
• Fallani, A.,  Medrano Sandonas, L. and Tkatchenko, A. Towards the inverse design of molecules with target quantum mechanical properties. In DQML Hintertux joint conference. February 2022.
• Masood, A., Heinonen, M., Herman, D., Ceulemans, H. Kaski, S. Dos-Time dependent DILI modeling.  In Finnish Center of Artificial Intelligence Virtual Drug Design Lab seminars. (February 8th, 2022)

2021

• Andronov, M.  Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction AIDD on-line seminar (December 8th, 2021)