Topic: drug discovery

Four faculty appointments develop and interpret deep neural networks for biology. Research areas include protein and enzyme design, drug discovery and drug-target interactions, immunobiology, neural activity, virtual cells, biological imaging, biomarker discovery and multi-omic or spatial datasets. Successful applicants establish funded research programmes and mentor graduate and postgraduate researchers. Applicants should identify suitable home departments and provide a research plan, mentoring statement, CV and references.

A one-day scientific exchange for students, early-career researchers, academics and professionals working in computational biophysics, structural biology, bioinformatics, computational chemistry and drug discovery. Presentations and interactive sessions examine computational approaches to drug design, with discussion, research-development conversations, mentorship and collaboration opportunities. The event emphasizes strengthening research connections in Ghana and the wider African scientific community. The programme runs from 08:00 to 17:00 GMT, including afternoon computational-biophysics an

Apply machine learning throughout drug discovery, from biological target identification to molecular screening. The workshop explains model design, training, evaluation and prediction interpretation, then develops deep-learning and graph-neural-network methods in PyTorch. Practical work turns molecular and omics data into model inputs, implements a graph model from a scientific paper, and examines disease classification and target discovery. The final day builds pipelines from research databases and introduces language-model fine-tuning using biological data. Live online, 30 November–3 Decemb

The Biophysical Society of Canada’s 12th annual meeting connects molecular, cellular and biomedical biophysics with computational methods, drug discovery, imaging, neuroscience, aging, nanobiotechnology, quantum biology and artificial intelligence. Hosted by the University of Waterloo with the University of Guelph and Wilfrid Laurier University, the in-person programme is expected to include a trainee symposium, invited and contributed talks, posters, professional development and industry engagement. Teaching, science communication and public engagement will also feature.

This EMBO Workshop examines how computational structural biology is moving from static structure prediction toward molecular dynamics, ensembles, biological context and design. Six scientific sessions cover integrative all-atom models and assemblies; nucleic acids and their protein interactions; physics-based simulations; machine-learned dynamics; biomolecular design and drug discovery; and function, evolution and mechanism. The programme combines invited and abstract-selected talks with a round-table discussion, posters, a job fair and networking. Two sessions focus on dynamics, alongside exp

Research new machine-learning architectures and training approaches for drug discovery. Design experiments, evaluate models and work with engineering and domain experts to scale promising methods into scientific applications. The position calls for original research together with practical implementation and clear communication across disciplines. Applications remain available on the employer’s role page, with no fixed deadline stated.

Research models that connect chemical structures, protein sequences and cellular transcriptomics to predict biological responses. Develop and test scientific hypotheses using large single-cell perturbation datasets, contributing new architectures and evaluations for drug discovery. The employer lists the role as on-site with at least three office days each week. The exact role page currently accepts applications and states no fixed deadline.

Translate frontier machine-learning prototypes into scalable systems for drug discovery. The role optimises models such as diffusion networks, transformers and DNA-sequence models in JAX or PyTorch, improves distributed training and inference, and develops research libraries and evaluation tools.

PharmSci 360 connects experimental, computational and clinical evidence across drug discovery, development and delivery. Researchers will examine in-vitro-to-human translation using PBPK, quantitative systems pharmacology and AI; organ-chip and other human-relevant models; biomarkers and reliable bioanalytical assays; and integrating clinical trials with real-world data. Manufacturing sessions connect process understanding, analytical characterization and digital models with quality and scale-up. Formulation sessions address predictive design, advanced materials, targeted delivery and patient

Rafael Rosengarten talks with NVIDIA’s Stacie Calad-Thomson about connecting AI-driven drug discovery to physical laboratory work. They examine why consistent experimental data is essential, how agents and robotics could coordinate experiments, and how scientists’ responsibilities may change as laboratories automate. The discussion distinguishes proposing molecules or experiments from executing them, validating results and deciding the next research step.

The sixth H3D symposium brings scientists and pharmaceutical researchers together around chemistry and biology for infectious diseases. The programme covers drug discovery, chemical biology, medicinal and synthetic chemistry, infectious-disease biology and enabling technologies, with scientific talks, posters and networking. It takes place at the Arabella Hotel, Golf and Spa outside Hermanus, South Africa, from 13 to 16 October 2026. Standard delegate registration is open until 10 October 2026. Abstract submission closed on 15 May and early registration closed on 31 July; those actions are no

Carnegie Mellon researcher and Abridge co-founder Zachary Lipton discusses where machine learning can help healthcare and why clinical work is harder to automate than software development. The conversation examines medical documentation, decision support, drug discovery, open models and model routing, then turns to academic research, doctoral training and the effects of automation on scientific careers. Hosted by Ravid Shwartz Ziv and Allen Roush. Watch the full conversation on the publisher’s YouTube channel.

Rafael Rosengarten and BioPharmaTrend co-founder Andrii Buvailo discuss how to assess progress in AI-assisted drug discovery. They trace the move from individual computational tasks to connected research workflows and question whether counting AI-associated drug candidates captures the technology’s value. Topics include research productivity, trial design, biomarkers, human expertise and the use of patient-derived data, organoids and other representative biological systems to improve translation. The conversation also considers why AI has become polarising and how specialised models may fit in

This English-language webinar examines advances in organoid research, production, and drug discovery. Hideya Sakaguchi covers the developmental-biology origins of neural organoid research and its potential for drug discovery, followed by a keynote from Thomas Mitchell of The Organoid Company and a live question session.

Tim Wahlberg of CAS speaks with Rafael Rosengarten about the role of curated chemical knowledge in AI-assisted drug discovery. They examine scientific data quality, harmonisation and expert curation, and consider how reliable information can help researchers formulate questions and evaluate answers from large language models.

The event is open to everyone interested in Neuroscience, Cell Biology, Drug Discovery, Disease Modeling, and Bio/Neuroengineering! This meeting is a platform bringing scientists from all over the world together and fostering scientific exchange and collaboration.

The event is open to everyone interested in Neuroscience, Cell Biology, Drug Discovery, Disease Modeling, and Bio/Neuroengineering! This meeting is a platform bringing scientists from all over the world together and fostering scientific exchange and collaboration.

Recent developments in induced pluripotent stem cell (iPSC) technology have enabled easier access to human cells in vitro. With increasing availability of human iPSC-derived neurons, both healthy and disease cell lines, screening compounds for neurodegenerative diseases on human cells can potentially be performed in the earlier stages of drug discovery. To accelerate the functional characterization of iPSC-derived neurons and the effect of compounds, reproducible and relevant results are necessary. In this webinar, the speakers will: Introduce high-resolution functional imaging of human iPSC

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by the progressive degeneration of midbrain dopamine neurons. Current treatments mainly rely on pharmacotherapy, which seems to provide beneficial effects only in the very early stages of the disease. Having an in vitro source of mature midbrain dopamine neurons from patients is paramount to initially reproduce disease phenotypes and subsequently identify disease-modifying drug candidates. In this context, three-dimensional (3D) induced pluripotent stem cell (iPSC)-derived brain organoids play an important role, as they mim

The flexibility and accessibility of induced pluripotent stem cell (iPSC) technology has allowed complex human biology to be reproduced in vitro at high throughput scales. Indeed, rapid advances in stem cell technology have led to widespread adoption for the development of in vitro models of neuron electrophysiology to be used in screening applications in drug discovery and safety. Furthermore, advanced cell preparations, such as spheroids or organoids, are under intense investigation with aims toward establishing mature physiologically relevant phenotypes in vitro. The objective of this work