Vibrant color portrait of Jane S. Richardson, the visionary biophysicist and artist who revolutionized structural biology with her invention of ribbon diagrams. She gazes warmly at the camera with a bright, knowing smile that radiates quiet brilliance and decades of curiosity. Her silver-blonde hair woven with gentle waves. Large, elegant dangling earrings catch the light, and she wears a richly patterned brown blouse embroidered with intricate turquoise paisley motifs and delicate beadwork that echoes the molecular elegance she has spent her life depicting. Behind her floats a luminous, dreamlike backdrop of glowing molecular structures--interlocking hexagonal and ribbon-like forms in electric blues, teals, and greens--blending science and art in a single, living canvas.

Hand-drawn and hand-colored (by Jane Richardson) scientific artwork known as a Richardson ribbon diagram (or “ribbon model”), one of the iconic visual inventions of Jane Richardson that transformed the way we see and understand protein structures. A graceful, three-dimensional tangle of protein backbone ribbons twists and spirals through space, rendered in soft pencil lines and luminous watercolor hues. Smooth golden-brown coils represent α-helices that curl like elegant ribbons, while broad teal-green arrows trace the flat, pleated strands of β-sheets slicing through the molecule with directional purpose. Thin, looping golden threads connect the secondary structures, creating a delicate, almost dance-like choreography of biology’s hidden architecture. The entire form is framed by a simple olive-green mat and dark border, giving the drawing the quiet dignity of both fine art and precise scientific illustration—a timeless bridge between molecular reality and human imagination.

Jane Richardson was born #OTD in 1941

+ Developed the Richardson (ribbon) diagram to represent proteins' 3D structure (becoming a standard representation for protein structures)
+ MacArthur Fellow, 1985
+ Elected, Nat'l Academy of Sciences, 1991
+ President, Biophysical Society, 2012

#WomenInSTEM

On the train back from the #CECAM workshop ‘RNA modeling across scales’. Big thanks to the organisers @bussilab.org, @magistratolab.bsky.social and @marcodevivo.bsky.social for the opportunity to present our two innovative approaches with @bonomimax.bsky.social: #HARIBOSS and #SHAMAN!

Computational Hit Finding: An Industry Perspective Computational hit finding, particularly virtual screening, has been a mainstay of drug discovery campaigns for decades, providing a cost-efficient complement to wet experiments. Innovation in this spa...

Really happy to have contributed to this perspective on computational hit finding approaches! Great team work with Steven Jerome, Fredrik Svensson, Carlos Roca Magadán and Marcel de Groot. #jmedchem #cadd #compchem
pubs.acs.org/doi/10.1021/...

Our integrative approach using #MD and #cryoEM data to construct structural ensembles of #RNA just published on @natcomms.bsky.social doi.org/10.1038/s414... Lead by Elisa Posani, with @magistratolab.bsky.social @bonomimax.bsky.social @pjanos.bsky.social @navtejtoor.bsky.social and Daniel Haack 🎉

📢 Our article calling for a #FAIR database for #MolecularDynamics simulation data has now been peer-reviewed and published in @naturemethods.bsky.social

📖 Read it here: rdcu.be/ef6YX

📝 Support the statement: bit.ly/3zVS3qm

#MDDB #FAIRdata #collaboration

PLUMED Tutorials: A collaborative, community-driven learning ecosystem In computational physics, chemistry, and biology, the implementation of new techniques in shared and open-source software lowers barriers to entry and promotes

The paper describing our community effort to collect and organize #plumed tutorials has been published in the Journal of Chemical Physics, as part of the Michele Parrinello Festschrift! doi.org/10.1063/5.02...

This is the moment we've worked toward for a long time: First public disclosure of the @asapdiscovery.bsky.social pan-coronavirus antiviral aiming to help keep humanity safe from future pandemic threats like MERS-CoV and other bat coronaviruses.

Harnessing Medicinal Chemical Intuition from Collective Intelligence Over the past decade, collective intelligence, i.e., the intelligence that emerges from collective efforts, has transformed complex problem-solving and decision-making. In drug discovery, decision-making often relies on medicinal chemistry intuition. The present study explores the application of collective intelligence in drug discovery, focusing on lead optimization. Ninety-two Sanofi researchers with diverse expertise participated anonymously in an exercise centered on ADMET-related questions. Their feedback was used to build a collective intelligence agent, which was compared to an artificial intelligence model. The study led to three major conclusions: first, collective intelligence improves decision-making in optimizing ADMET endpoints, compared to individual decisions. Second, collective intelligence outperforms artificial intelligence for all other endpoints but hERG inhibition. Finally, we observe complementarity between collective human and artificial intelligence. Overall, this research highlights the potential of collective intelligence in drug discovery and the importance of a synergistic approach combining human and artificial intelligence in project decision making.

A single chemist might guess—but can a collective outsmart AI in drug discovery?

In our recent study at Sanofi (J. Med. Chem.), 92 researchers put collective intelligence to the test against AI models on lead optimization tasks.

The results? Click below!
pubs.acs.org/doi/full/10....

#jmedchem with @PierreLlompart

Harnessing Medicinal Chemical Intuition from Collective Intelligence Over the past decade, collective intelligence, i.e., the intelligence that emerges from collective efforts, has transformed complex problem-solving and decision-making. In drug discovery, decision-mak...

Really proud of our most recent publication that leverages the power of medicinal chemistry intuition and collective intelligence pubs.acs.org/doi/10.1021/...