Ben Fry
@benf549.bsky.social
PhD Candidate @ Harvard Biophysics Program
ML for Small-Molecule Binding Protein Design
Polizzi Lab at Dana Farber Cancer Institute
🏳️🌈
ML for Small-Molecule Binding Protein Design
Polizzi Lab at Dana Farber Cancer Institute
🏳️🌈
Reposted by Ben Fry
@benf549.bsky.social made a nice little google colab notebook for running LASErMPNN for protein sequence design conditioned on ligands. Check it out! Feedback welcome. colab.research.google.com/github/poliz...
Google Colab
colab.research.google.com
November 13, 2025 at 7:14 PM
@benf549.bsky.social made a nice little google colab notebook for running LASErMPNN for protein sequence design conditioned on ligands. Check it out! Feedback welcome. colab.research.google.com/github/poliz...
Reposted by Ben Fry
The paper represents a paradigm shift by combining machine-learned interatomic potentials (MLIPs) with generative modeling to bypass traditional conformer generation, achieving both higher accuracy and greater efficiency than existing methods. Free & open source: github.com/isayevlab/LoQI
GitHub - isayevlab/LoQI: LoQI: Low Energy QM Informed Conformer Generation
LoQI: Low Energy QM Informed Conformer Generation. Contribute to isayevlab/LoQI development by creating an account on GitHub.
github.com
August 20, 2025 at 4:45 PM
The paper represents a paradigm shift by combining machine-learned interatomic potentials (MLIPs) with generative modeling to bypass traditional conformer generation, achieving both higher accuracy and greater efficiency than existing methods. Free & open source: github.com/isayevlab/LoQI
Reposted by Ben Fry
Interested in doing a postdoc at DFCI/Harvard on computationally designing and experimentally characterizing mini-protein binders for biomedical applications? Eric Fischer and I are looking for someone to work in our groups starting asap! Email me or my admin with a CV to apply!
August 19, 2025 at 4:21 PM
Interested in doing a postdoc at DFCI/Harvard on computationally designing and experimentally characterizing mini-protein binders for biomedical applications? Eric Fischer and I are looking for someone to work in our groups starting asap! Email me or my admin with a CV to apply!
Reposted by Ben Fry
🚨New paper 🚨
Can protein language models help us fight viral outbreaks? Not yet. Here’s why 🧵👇
1/12
Can protein language models help us fight viral outbreaks? Not yet. Here’s why 🧵👇
1/12
August 17, 2025 at 3:42 AM
🚨New paper 🚨
Can protein language models help us fight viral outbreaks? Not yet. Here’s why 🧵👇
1/12
Can protein language models help us fight viral outbreaks? Not yet. Here’s why 🧵👇
1/12
Reposted by Ben Fry
The biggest challenge for AI in biology isn't just models, it's the data used to train them. Standard biological data isn't built for AI. To unlock generative AI for drug discovery, we must rethink how we generate and capture data. 1/
July 22, 2025 at 12:30 PM
The biggest challenge for AI in biology isn't just models, it's the data used to train them. Standard biological data isn't built for AI. To unlock generative AI for drug discovery, we must rethink how we generate and capture data. 1/
Reposted by Ben Fry
📢 Congratulations 𝗗𝗿. 𝗙𝗿𝗮𝗻𝘇𝗶𝘀𝗸𝗮 𝗦𝗲𝗻𝗱𝗸𝗲𝗿 for receiving the Otto Hahn Medal and Otto Hahn Award from @maxplanck.de ! 🎉 The honors recognize her exceptional work @georghochberg.bsky.social. 🌟 Exciting times ahead! #MaxPlanck #ResearchExcellence www.mpi-marburg.mpg.de/1511259/2025...
Triple Honours for Franziska Sendker
Dr Franziska Sendker, a former doctoral candidate at the Max Planck Institute for Terrestrial Microbiology, has been awarded the Otto Hahn Medal by the Max Planck Society. The medal honours the outstanding achievements of young scientists and comes with a prize of 7,500 euros. In March 2025, she received the Bayer Pharmaceuticals Doctoral Award from the Society for Biochemistry and Molecular Biology (GBM e.V.), presented at the Mosbach Colloquium. Franziska Sendker's research showed that complex protein forms can arise not only through natural selection, but also through random genetic changes.
www.mpi-marburg.mpg.de
June 26, 2025 at 1:04 PM
📢 Congratulations 𝗗𝗿. 𝗙𝗿𝗮𝗻𝘇𝗶𝘀𝗸𝗮 𝗦𝗲𝗻𝗱𝗸𝗲𝗿 for receiving the Otto Hahn Medal and Otto Hahn Award from @maxplanck.de ! 🎉 The honors recognize her exceptional work @georghochberg.bsky.social. 🌟 Exciting times ahead! #MaxPlanck #ResearchExcellence www.mpi-marburg.mpg.de/1511259/2025...
Trying out the Boltz-2 affinity prediction on the Exatecan binders we generated with LASErMPNN and NISE. Affinity prediction still clearly has room to improve, but the model seems to be able to identify the highest affinity mutant in this small dataset. Thanks to @gcorso.bsky.social and team! 1/2
June 7, 2025 at 12:22 AM
Trying out the Boltz-2 affinity prediction on the Exatecan binders we generated with LASErMPNN and NISE. Affinity prediction still clearly has room to improve, but the model seems to be able to identify the highest affinity mutant in this small dataset. Thanks to @gcorso.bsky.social and team! 1/2
Reposted by Ben Fry
from most recent Harvard lawsuit. sums it up pretty succinctly I think
May 27, 2025 at 2:36 PM
from most recent Harvard lawsuit. sums it up pretty succinctly I think
Reposted by Ben Fry
in case you missed the superb seminar that Ben Fry gave back in January, you can now check out the recording youtu.be/IgFgAYQrke4
and the preprint www.biorxiv.org/content/10.1...
and the preprint www.biorxiv.org/content/10.1...
Design of small molecule binding proteins using deep learning
YouTube video by Boston Protein Design and Modeling Club
youtu.be
May 3, 2025 at 2:07 PM
in case you missed the superb seminar that Ben Fry gave back in January, you can now check out the recording youtu.be/IgFgAYQrke4
and the preprint www.biorxiv.org/content/10.1...
and the preprint www.biorxiv.org/content/10.1...
Reposted by Ben Fry
We're super excited by the method. We think it can help to rapidly produce binders to small molecules for sensors, antidotes, delivery vehicles, even enzymes. Let us know what you think and please try it out! Finally, shout out to Ben and Kaia for making this all happen!!! 🤩
April 28, 2025 at 3:22 PM
We're super excited by the method. We think it can help to rapidly produce binders to small molecules for sensors, antidotes, delivery vehicles, even enzymes. Let us know what you think and please try it out! Finally, shout out to Ben and Kaia for making this all happen!!! 🤩
Reposted by Ben Fry
Lastly, Kaia checked to see if EPIC and its higher affinity mutant are able to protect exatecan from hydrolysis, which is not something serum albumin can do. For a drug that normally hydrolyzes in a few hours, EPIC was able to stabilize the lactone form for days! ✅
April 28, 2025 at 3:22 PM
Lastly, Kaia checked to see if EPIC and its higher affinity mutant are able to protect exatecan from hydrolysis, which is not something serum albumin can do. For a drug that normally hydrolyzes in a few hours, EPIC was able to stabilize the lactone form for days! ✅
Reposted by Ben Fry
Since EPIC and exatecan aren't in the PDB, we wanted to see how co-structure predictors do on it. They each get the backbone right but differ at the ligand. The pose is correct but the modeling of the conformer is wonky. AF3 does the best. AF3 is also able to rank affinities via pLDDT of ligand! 😱
April 28, 2025 at 3:22 PM
Since EPIC and exatecan aren't in the PDB, we wanted to see how co-structure predictors do on it. They each get the backbone right but differ at the ligand. The pose is correct but the modeling of the conformer is wonky. AF3 does the best. AF3 is also able to rank affinities via pLDDT of ligand! 😱
Reposted by Ben Fry
Kaia was able to crystalize EPIC and determine its structure to 2.0 Å resolution. It agreed pretty well with the LASEr design! RFAA had a hard time modeling the lactone ring of the drug, so there is some disagreement there. The lactone is buried as intended, and the goal was to hide it from water 👍
April 28, 2025 at 3:22 PM
Kaia was able to crystalize EPIC and determine its structure to 2.0 Å resolution. It agreed pretty well with the LASEr design! RFAA had a hard time modeling the lactone ring of the drug, so there is some disagreement there. The lactone is buried as intended, and the goal was to hide it from water 👍
Reposted by Ben Fry
Ben didn't stop there. He wanted to improve affinity of EPIC for exatecan using computation alone. He used LASErMPNN to "proofread" EPIC's sequence using a predicted co-structure as input. LASEr suggested two mutations. Kaia verified that each improved binding 10x. 100x when combined (1 nM Kd)!
April 28, 2025 at 3:22 PM
Ben didn't stop there. He wanted to improve affinity of EPIC for exatecan using computation alone. He used LASErMPNN to "proofread" EPIC's sequence using a predicted co-structure as input. LASEr suggested two mutations. Kaia verified that each improved binding 10x. 100x when combined (1 nM Kd)!
Reposted by Ben Fry
Kaia Slaw (no bluesky) experimentally tested 4 designs from NISE and 16 from COMBS. All 4 NISE designs bound! The highest affinity binder- which Ben and Kaia call "EPIC" - was pretty tight (0.1 uM Kd). Compared to COMBS (3 of 16 bound, tightest was 10 uM), NISE and LASErMPNN did a much better job!
April 28, 2025 at 3:22 PM
Kaia Slaw (no bluesky) experimentally tested 4 designs from NISE and 16 from COMBS. All 4 NISE designs bound! The highest affinity binder- which Ben and Kaia call "EPIC" - was pretty tight (0.1 uM Kd). Compared to COMBS (3 of 16 bound, tightest was 10 uM), NISE and LASErMPNN did a much better job!
Reposted by Ben Fry
Ben used NISE and LASErMPNN to design binders to exatecan, an anticancer drug prone to inactivation by hydrolysis. We also used a more "traditional" approach using COMBS and Rosetta to design binders. We could compare the methods head to head.
April 28, 2025 at 3:22 PM
Ben used NISE and LASErMPNN to design binders to exatecan, an anticancer drug prone to inactivation by hydrolysis. We also used a more "traditional" approach using COMBS and Rosetta to design binders. We could compare the methods head to head.
Reposted by Ben Fry
With the new co-structure predictors like RFAA, Boltz-1, and AF3, we can now extend self-consistency into the ligand dimension. And Ben's NISE algorithm maximizes this. Code repo here: github.com/polizzilab/N...
April 28, 2025 at 3:22 PM
With the new co-structure predictors like RFAA, Boltz-1, and AF3, we can now extend self-consistency into the ligand dimension. And Ben's NISE algorithm maximizes this. Code repo here: github.com/polizzilab/N...
Reposted by Ben Fry
We all know in protein design about the goal of self consistency. That is, we want the predicted structure to look like the structure for which we designed the sequence.
April 28, 2025 at 3:22 PM
We all know in protein design about the goal of self consistency. That is, we want the predicted structure to look like the structure for which we designed the sequence.
Reposted by Ben Fry
Ben used LASErMPNN in combination with a protein-ligand co-structure predictor, RFAA, in an iterative algorithm called NISE that refines designs. NISE optimizes the sequence, structure, and ligand conformer together to improve the confidence of both models. It's a neural-network-only algorithm
April 28, 2025 at 3:22 PM
Ben used LASErMPNN in combination with a protein-ligand co-structure predictor, RFAA, in an iterative algorithm called NISE that refines designs. NISE optimizes the sequence, structure, and ligand conformer together to improve the confidence of both models. It's a neural-network-only algorithm
Reposted by Ben Fry
Ben Fry (@benf549.bsky.social) was excited when proteinMPNN came out, which motivated him to train a new gNN called LASErMPNN to design sequences given protein-ligand co-structure. LASErMPNN does pretty well at this! The repo is available and even has the training code! github.com/polizzilab/L...
April 28, 2025 at 3:22 PM
Ben Fry (@benf549.bsky.social) was excited when proteinMPNN came out, which motivated him to train a new gNN called LASErMPNN to design sequences given protein-ligand co-structure. LASErMPNN does pretty well at this! The repo is available and even has the training code! github.com/polizzilab/L...
Reposted by Ben Fry
Super excited to share a new preprint from our lab on design of small-molecule binding proteins using neural networks! The paper has a bit of everything. A new graph neural network, new design algorithms, and experimental validation. www.biorxiv.org/content/10.1...
🧵🧪
🧵🧪
Zero-shot design of drug-binding proteins via neural selection-expansion
Computational design of molecular recognition remains challenging despite advances in deep learning. The design of proteins that bind to small molecules has been particularly difficult because it requ...
www.biorxiv.org
April 28, 2025 at 3:22 PM
Super excited to share a new preprint from our lab on design of small-molecule binding proteins using neural networks! The paper has a bit of everything. A new graph neural network, new design algorithms, and experimental validation. www.biorxiv.org/content/10.1...
🧵🧪
🧵🧪