Enric Herrero - Exploring the Exa-space with 3D-based screening methods

We are happy to announce our speaker, Enric Herrero, presenting Exploring the Exa-space with 3D-based screening methods

Please join us for a in-person event

Date: Thursday, March 23, 2023 at 6:00 PM

Sign-up: BAGIM Meetup

Title:  Exploring the Exa-space with 3D-based screening methods

Abstract:

Many huge commercial libraries have appeared in the last 5 years that include Billions of molecules with a reasonable synthesizability and represent a huge opportunity for the drug discovery ecosystem. Bigger libraries can increase the chances of finding interesting hits in a drug discovery project but also represent a challenge to process with existing in-silico methods.

Several approaches have been proposed to deal with these chemical spaces that use simple molecule representations to contain the computational and storage cost. However, 3D representations add valuable binding information of the ligands and representations that are heavily tied to the chemical structure tend to limit the chemical diversity of the proposed solutions.

In this talk we are going to discuss how to tackle the problem of exploring huge chemical spaces in the Exa-scale without sacrificing the advantages of 3D based methods. Different approaches will be presented and compared in order to see the advantages and disadvantages of different molecule representations and screening methods and create a debate around the topic.

Soumya Raychaudhuri: Using genetics & genomics to define mechanisms autoimmunity

We are happy to announce our latest speaker Soumya Raychaudhuri, Professor of Medicine and Biomedical Informatics, Harvard Medical School, https://immunogenomics.hms.harvard.edu/

Please join us for a virtual event on

Date: April 12, 2023

Time: noon ET / 9 am PT

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Title: Using genetics and genomics to define mechanisms of autoimmunity

Abstract: To be confirmed

About the speaker:

Dr. Soumya Raychaudhuri is an Associate Professor at Harvard Medical School and at Brigham and Women’s Hospital. He is also appointed as an Associate Member at the Broad Institute and a Visiting Professor in Genetics at the University of Manchester. He matriculated into the Stanford University NIH funded MST program in 1997 after completing degrees in mathematics and biophysics at the University at Buffalo. In 2004, he completed both his medical training and his doctoral training in biomedical informatics. After completing his clinical training in Internal Medicine, he joined the rheumatology fellowship training program in 2006, and concurrently completed his postdoctoral fellowship training under Mark Daly at the Broad Institute. Since starting his own group in 2010 at Harvard Medical School and Brigham and Women’s Hospital, his lab has focused on finding and fine-mapping disease alleles and understanding their significance in immune-mediated diseases. He has focused on multiple diseases including rheumatoid arthritis, type I diabetes, and tuberculosis infection. He has worked on fine-mapping HLA loci, devising integrative statistical genetics strategies to identify causal variation by taking advantage of largescale epigenetic data, and integrating genetic data with functional genomics and data on human immunological phenotypes.

Michael Bellucci - Intelligent Cloud-Based Algorithms for Reducing Risk in Crystallization Process Development

We will be planning a September BAGIM in-person event with XtalPi. Registration to open on Wednesday, August 16, 2023

Michael Bellucci - Reducing Risk in Crystallization Process Development, Wed, Sep 13, 2023, 5:30 PM | Meetup

Date: September 13, 2023

Location: TBD (Cambridge, MA)

Attendance: 70 (maximum)

Mixer and Presentation: 5:30 PM - 7:00 PM

Welcome, Introductions, Presentation, Q&A, Conversation

Host: Dr. Sivakumar Sekharan, Senior Director Business Development

Speaker: Dr. Michael Bellucci, Senior Director, Solid Form Design

Abstract Title: Intelligent Cloud-Based Algorithms for Reducing Risk in Crystallization Process Development

Michael A. Bellucci*,1, Anke Marx*,2, Bing Wang1, Liwen Fang1, Yunfei Zhou1, Chandler Greenwell1, Zhuhong Li1, Dirk Wandschneider2, Jan Gerit Brandenburg2, Guangxu Sun1, Sivakumar Sekharan1, Axel Becker2

1 XtalPi, Inc., 245 Main Street, Cambridge, MA 02142

2 Merck KGaA, Frankfurter Str. 250, A022/001, 64293 Darmstadt, Germany

Crystallization is the most widely used separation and purification process in the pharmaceutical industry. The resulting crystal structure and corresponding crystal morphology isolated from this process can have a profound influence on the physical properties and manufacturability of drug product APIs. Consequently, the ability to characterize the crystal polymorph landscape and control the crystal morphology are two fundamental aspects of pharmaceutical manufacturing. At XtalPi, we have developed a cloud-based computational platform that combines advanced physics-based algorithms with A.I/machine learning algorithms in order to mitigate polymorph risk and support rational design of crystallization experiments for improved morphological control. We highlight various applications from our Crystal Structure Prediction and Morphology platforms and discuss our recent investigation of the effect of polymer additives on the crystal growth of metformin HCl. This study was performed both with experiments and computational methods with the aim of developing a combined screening approach for crystal shape engineering. Additionally, we have developed analysis methods to characterize the morphology “landscape” and quantify the overall effect of solvent and additives on the predicted crystal habits. Further analysis of our molecular dynamics simulations was used to rationalize the effect of additives on the growth rate of specific crystal faces.

Eric Sigel - Exploring Predictive Workflows in Small Molecule Discovery through DEL-ML

BAGIM is excited to announce our first in-person event of 2023. We are partnering with TandemAI on this event with invited speaker Eric Sigel.

Date: March 1, 2023

Time: Event time: 5:30 - 7:30PM - Talk promptly starts at 6 pm ET with Q&A immediately afterwards. Please arrive no later than 6 pm.

We will be serving complimentary drinks and refreshments.

Sign-up: BAGIM Meetup

Location: The Royal Sonesta, Riverfront Room - East Tower, Level 2, 40 Edwin Land Blvd., Cambridge, MA 02142

Title: Exploring Predictive Workflows in Small Molecule Discovery through DEL-ML

Abstract: AI/ML has rapidly moved from being a curiosity to center stage across the biotech industry. Novel approaches are being enabled by these new methods and the increasing ability to generate the large-scale data needed to drive the technology. However, the impact of these rapid advances is limited by an industry that is only beginning to adapt to the new paradigm. The presentation will use DEL-ML (Machine Learning on DNA Encoded Library data) to explore the predictive workflow including results of its application, needed shifts in the small molecule discovery mindset, and directions where the technology can move from here.

John Irwin: Chemical screening library growth: is bigger better?

We are happy to announce our latest speaker John Irwin, UCSF Pharmaceutical Chemistry, http://irwinlab.compbio.ucsf.edu

Please join us for a virtual event on

Date: January 11, 2023

Time: noon ET / 9 am PT

Sign-up - BAGIM Meetup

Title: Chemical screening library growth: is bigger better?

Abstract: To be confirmed

About the speaker:

John Irwin is Adjunct Associate Professor in the Department of Pharmaceutical Chemistry at the University of California San Francisco. John works on methods to discover new reagents for biological targets using target- and ligand-based methods. He applies these methods to various projects and participates in numerous collaborations.

John develops the ZINC database of commercially available compounds for virtual screening, the DUDE database for benchmarking virtual screening methods, the DOCK Blaster family of products for web-based virtual screening, and the SEA method of comparing proteins by the ligands they bind.

Short bio: John received his Ph.D. for work with Jack Dunitz in chemical crystallography in 1991 at ETH Zurich. He worked at a startup molecular modeling company before joining Gerard Bricogne's group at the Medical Research Council's Laboratory of Molecular Biology in Cambridge UK, developing software for new macromolecular crystallographic structure solution and refinement methods (BUSTER and SHARP). He spent a year and a half as a staff scientist at EMBL-EBI also in Cambridge working on the PDB cleanup project before moving to Northwestern University Medical School in 2000. He has been working on DOCK, ZINC, and SEA since 2000.

Alex Wiltschko - Digitizing olfaction for health and happiness

BAGIM is excited to welcome members back for our first in-person (CONFIRMED) event since 2020!

Date: December 14, 2022

Time: 6 pm ET

Location: Google - 150 Broadway, Cambridge, MA 02142

Sign-up - BAGIM Meetup

Alex Wiltschko will present "Digitizing olfaction for health and happiness: progress and opportunities"

Abstract: Computers can see and hear, but they cannot smell. We know living noses can smell COVID-19, people trapped in rubble, cancer, and Parkinson's disease. If we could digitize smell, people would live longer, happier lives. But how can we measure a smell? Smells are produced by molecules that waft through the air, enter our noses, and bind to sensory receptors. Potentially billions of molecules can produce a smell, so figuring out which ones produce which smells is difficult to catalog or predict. Sensory maps can help us solve this problem. Color vision has the most familiar examples of these maps, from the color wheel we each learn in primary school to more sophisticated variants used to perform color correction in video production. While these maps have existed for centuries, useful maps for smell have been missing, because smell is a harder problem to crack: molecules vary in many more ways than photons do; data collection requires physical proximity between the smeller and smell (we don’t have good smell “cameras” and smell “monitors”); and the human eye only has three sensory receptors for color while the human nose has > 300 for odor. As a result, previous efforts to produce odor maps have failed to gain traction.

We introduce the “Principal Odor Map” (POM), which identifies the vector representation of each odorous molecule in the model’s embedding space as a single point in a high-dimensional space. The POM has the properties of a sensory map: first, pairs of perceptually similar odors correspond to two nearby points in the POM (by analogy, red is nearer to orange than to green on the color wheel). Second, the POM enables us to predict and discover new odors and the molecules that produce them. We demonstrate that the map can be used to prospectively predict the odor properties of molecules, understand these properties in terms of fundamental biology, and tackle pressing global health problems. We discuss each of these promising applications of the POM and how we test them.

Digitizing Smell: Using Molecular Maps to Understand Odor

Digitizing Smell: Using Molecular Maps to Understand Odor – Google AI Blog (googleblog.com)

Machine Learning Highlights a Hidden Order in Scents

AI Model Links Smell Molecules With Metabolic Processes | Quanta Magazine

Prof. Charlotte Deane - From Machine learning to the physics of binding

 Details

Please join us for a BAGIM/SAGIM joint event sponsored by CCDC.

Date: December 7th, 2022

Time: 10 am PT/1 pm ET/6 pm BT

Sign-up - BAGIM Meetup

Title: From Machine learning to the physics of binding

Abstract:

Fueled by the success of machine learning in a wide range of domains, there is significant interest in the application of machine learning to early-stage drug discovery in areas from designing novel compounds to screening libraries of compounds against a specific target.

There has been particular interest in machine-learning based scoring functions for predicting the binding of small molecules to target proteins. The aim of these functions is to approximate the distribution which takes two molecules as input and outputs the energy of their interaction. This distribution is dependent on interactions between the atoms of the two molecules and the solvent, and only a scoring function that accounts for these interactions can accurately predict binding affinity on novel/unseen molecules.

To attempt to create a method capable of learning these interactions we built PointVS, a machine learning scoring function, which achieves state-of-the-art performance even after performing rigorous filtering of the training set. PointVS is able to identify important interactions. PointVS appears able to identify important binding interactions and is the first deep learning-based method for extracting important binding information from a target for molecule design.

About Speaker: 

Charlotte Deane, a professor of Structural Bioinformatics and former Head of the Department of Statistics at the University of Oxford. She completed her undergraduate education at University College, Oxford and went to the University of Cambridge to study structural bioinformatics. Prof. Deane worked as a Wellcome Trust Research Fellow for two years. She was recently awarded an MBE in the Queen’s Birthday Honours. Her research focuses on protein structure prediction, particularly antibodies. Her research group, Oxford Protein Informatics Group (OPIG), created the SABDab, a database for antibody structures and the SAbPred, a webserver for antibody structure prediction. In addition, Prof. Deane's research also focuses on immuninformatics, biological networks and small molecule drug discovery.