Twill is The Intelligent Healing Company™. We shorten the distance between need and care by configuring personalized digital therapeutics and care solutions at scale for the modern healthcare cloud. Our platform integrates AI with empathy, making healing more personal, precise, and connected for the entire care journey. We deploy a full spectrum of clinical-grade care solutions—including Digital Therapeutics, Coaching, Community, and Well-being products—for pharma, health plans, enterprises, and individuals everywhere.
Our global platform is available in 10 languages, supports more than 10 chronic conditions, and covers more than 20 million lives.
QC Ware enables enterprises to leverage the latest computational power to accelerate innovation while preparing them for quantum supremacy. A quantum market leader, they offer software and services across biotech and pharmaceuticals, financial services, aerospace, energy, and more. Clients include Roche, Boehringer Ingelheim, Covestro, Goldman Sachs, and BMW. QC Ware is the organizer of Q2B, the first and largest gathering of the international quantum computing community.
Our physics-based computational platform leverages a deep understanding of physics, chemistry, and predictive modeling to accelerate innovation.
Our platform enables our collaborators to discover high-quality, novel molecules more rapidly, at lower cost, and we believe with a higher likelihood of success compared to traditional methods. We’re also harnessing this platform for our internal drug discovery programs.
We are proud to be leading this digital revolution.
Genomenon’s AI-driven genomic engine leverages billions of genetic associations between diseases, phenotypes, and therapies found within the medical evidence. They deliver a comprehensive genomic landscape for every disease, including rare, neurodegenerative, and genetic diseases, as well as somatic and germline cancer.
Armed with a comprehensive base of molecular biomarkers and disease mechanisms, Genomenon’s customers expand their genetic knowledge of the disease drivers by a factor of 10-20X to accelerate target discovery, identify genetic biomarkers for better clinical trial stratification, and develop CDx for regulatory approval
Deep dive into the biggest data and cultural hurdles, plus organizational challenges hindering the widespread adoption of AI in drug discovery. Bringing case studies from leveraging AI for drug discovery, small molecule drug design and using NLP in drug discovery under the microscope, this summit will highlight the latest innovations in the industry.
AI in Pharma: Discovery brings together senior leaders from across the pharmaceutical industry to showcase cutting edge developments. This summit provides an opportunity to get exclusive insights from experts from Pfizer, Janssen, Relay Therapeutics, Novartis, Genentech and more.
With the AI in Pharma Summit now split into two dedicated days for Discovery and Clinical Trials, each day will deep-dive into the crucial, specific challenges for each stage of the drug development process. This co-located event provides a comprehensive offering, bringing the industry together under one roof for one or two days of learning.
I’m looking forward to participating in this meeting to advance the discussion on how we can leverage AI to transform the trajectory of healthcare. AI is fundamentally changing how we innovate – enabling us to accelerate R&D and drive greater patient impact than ever before.
I am looking forward to the AI in Pharma: Discover conference because it brings together key AI thought leaders across Pharma. At the conference, I am looking forward to forging new relations and discussing how we can maximize the value of AI to benefit patient treatments.
At the AI in Pharma: Discovery conference, I am looking forward to discussing how the background, training, and typical styles of work of chemists and computational scientists create barriers to effective communication and collaboration between the groups. This aspect of a successful experimental-computational partnership does not get enough attention and is at least as important as the many technical issues that we spend so much time discussing.
I’m looking forward to the AI in Pharma: Discovery conference to discuss how we use AI to enhance our ability to discover drugs. When used by experienced drug discovery researchers, AI is a powerful tool that can save years in the R&D process and improve success rates
Pavan Choksi is a Partner at Arkitekt Ventures, an early-stage venture fund with a mission to improve and advance human health. At Arkitekt, he specializes in backing founders working on disruptive models of healthcare delivery, digital health platforms and applications of frontier technologies such as neurotech, bio-engineering, quantum & AI to medicine. Pavan brings deep healthcare operating expertise in business development and commercialization strategy as he led enterprise business development at Capsule, a digital pharmacy unicorn startup that has raised over $570M of capital to date. Previously, he was co-founder & VP of Business Development of Rx.Health, a venture-backed Mount Sinai Health System company. As an active member of the digital health ecosystem, he has served on the Strategic Advisory Boards of the Digital Medicine Society, Digital Therapeutics Alliance, NODE Health, and Old Silver VC.
Jiye Shi is an Associate Vice President and Global Head of Computational Chemistry, Cheminformatics & Automation Platforms at Eli Lilly and Company. Prior to that, he was a NewMedicines Fellow and Global Head of CADD at UCB Pharma. During the past 20 years, Jiye and his teams utilized physics, statistics and machine learning/AI based methods to accelerate the discovery of both small molecule drugs and biologics. He is a co-inventor on 11 patents which led to 2 approved drugs on the market.
Jiye Shi received his PhD in Computational Structural Biology with Prof. Sir Tom Blundell at the University of Cambridge and conducted his executive MBA study at the University of Rochester. He is an external advisor to the Department of Statistics, University of Oxford and helped establish and manage doctoral training centers. He holds visiting academic appointments in several countries and has co-authored over 200 research papers.
Dr. Tommaso Mansi is VP of Artificial Intelligence (AI) and Digital Health, Data Science, at Janssen R&D. He holds a PhD in biomedical engineering from INRIA Sophia Antipolis, France. Afterwards, Dr. Mansi worked at Siemens Healthineers, Digital Technology and Innovation, where he took roles of increasing responsibility and eventually lead a team focusing on the development and translation of AI solutions for image-guided therapy and robotics. He then joined Janssen R&D, Data Science, in 2021. In his current position, Dr. Mansi focuses on the research and development of AI solutions spanning digital health, computer vision, and biology, to derive advanced insights from multimodal, biomedical data and accelerate drug discovery and development. Throughout his career, Dr. Mansi and the teams he worked with received several awards and gave multiple keynotes at international conferences. He holds 70+ granted US patents, co-edited 1 monograph and co-authored 100+ scientific publications.
Bevan Emma Huang, Ph.D., is the Head of Pharmaceutical Data Sciences for the Lung Cancer Initiative, where she leads a team supporting drug discovery and development by unlocking value from data generated in collaborations, preclinical and clinical studies. She is an experienced data scientist with years of research leadership in integrating diverse data modalities to generate efficiencies and novel insights. Emma previously served as the Global Early Innovation Partnering Lead for Data Sciences at J&J Innovation, where she sourced, evaluated, and facilitated external partnering opportunities in data sciences across all sectors of Johnson & Johnson. During this time, she was the inaugural J&J Program Co-Director for the Innovate For Health Data Science Fellowship Program, an initiative established by UCSF, UC Berkeley, and J&J to develop leaders who will transform healthcare by applying data science to solve high-impact unmet needs. From 2015-2018, she led a multidisciplinary team in Janssen R&D, developing and supporting precision medicine initiatives through integrating multiple data modalities such as omics, sensors, and electronic health records for risk prediction, target, and biomarker discovery. Prior to joining J&J, Emma held positions at the Commonwealth Scientific and Industrial Research Organization (CSIRO) advancing precision agriculture approaches. Emma received her B.S. in Mathematics from the California Institute of Technology, her Ph.D. in Biostatistics from the University of North Carolina at Chapel Hill, and completed post-doctoral studies at CSIRO in Brisbane, Australia.
Peter Henstock is the Machine Learning & AI Lead at Pfizer and based in greater Boston. His work has focused at the intersection of AI, visualization, statistics and software engineering applied mostly to drug discovery but more recently to clinical trials. Peter holds a PhD in Artificial Intelligence from Purdue University along with 6 Master’s degrees. He was recognized as being among the top 12 leaders in AI and Pharma globally by the Deep Knowledge Analytics group. He also currently teaches graduate AI, and Software Engineering courses at Harvard.
For the last 20 years I’ve built teams that leverage machine learning to solve impossible
problems. I’ve been part of early stage startups, large organizations, and companies making
the transition between the two. I have several successful products I can point to, and few
that failed dramatically. Probably learned more from the latter.
I’m currently at GSK working on applying AI to finding treatments to disease that wouldn’t
be found with traditional methods.
Yinghao Ma is an Assistant Director at American Chemical Society (ACS). He has over 20 years’ experience in software engineering. In the last 6 years, he has been leading a software development team at ACS to build AI powered products, including recommendation systems, content classifiers, and editorial assistant tools to improve content delivery and peer review processes. He received his M.S. in computer and information science from the Ohio State University, and B.E in computer science from Zhejiang University, China.
Dr. Cornelis “Marcel” Hop is Vice-President at Genentech and supervising the DMPK
department. He leads a team of about 85 scientists involved in acquisition and
interpretation of ADME data in support of drug discovery and development ranging from
early stage research to NDA and beyond. Before that, he was a Senior Director at Pfizer
and a Senior Research Fellow at Merck. He has extensive experience in ADME sciences
with a particular focus on PK optimization, human PK prediction, biotransformation,
bioanalysis and the use of artificial intelligence and machine learning in drug discovery.
He has authored more than 185 publications and made more than 85 external oral
presentations. In addition, he co-authored one of the best-selling books in the ADME
field: Drug Metabolism and Pharmacokinetics Quick Guide.
Bino John, PhD, currently leads a global computational biology team at AstraZeneca (AZ). The team works on building next-generation capabilities to accelerate drug discovery and development, by combining data science, AI, biological, and patient data. Before joining AZ in 2018, Bino led a variety of computational biology initiatives and teams at Dow and then Dow-DuPont. In those roles, his efforts included enabling machine learning/AI and integrative big-data informatics capabilities for genomics research. He earned an Integrated Master’s degree in Chemistry from the Indian Institute of Technology (Mumbai) in 2000 and subsequently received his PhD from The Rockefeller University in Biomedical Sciences in 2003. His thesis research in computational structural biology with Dr. Andrej Sali was followed by postdoctoral studies in computational genomics with Dr. Chris Sander at the Memorial Sloan-Kettering Cancer Centre. In 2005, Bino joined the University of Pittsburgh as a faculty, where he focused on using high-throughput methods for cancer biomarker discovery, resulting in the discovery of novel molecules and molecular pathways.
Dr. Fahimeh Mamashli is Associate Director in Digital Medicine Data Science at Pfizer and Research Faculty in Harvard Medical School. Her background is Biomedical Engineering and she holds a PhD in Neuroscience from Max Planck Institute in Germany. She worked in Martinos Center for Biomedical Imaging and Harvard Medical School as a postdoc since 2014 and promoted to faculty in 2019. In Martinos Center, she worked on advance signal processing, statistics and machine learning. She joined Takeda Data Engineering and AI group in 2020. In her current position at Pfizer, Dr. Mamashli focuses on research and development of AI solutions in digital health using wearables and speech data. Throughout her career, she has received multiple awards, co-authored 15+ scientific publications and was invited to present her work in international conferences.
Patrick Riley leads the artificial intelligence group at Relay Therapeutics, applying learning methods to the discovery process. He has over 15 years of data science and machine learning experience. He came to Relay Therapeutics from Google, where he was a principal software engineer and a lead of the Accelerated Science team. His work spanned areas as diverse as cell imaging, nuclear fusion and materials science. His most important work was on the application of machine learning to small molecules, including foundational work on graph neural networks and their application to DNA encoded small molecule library screening.
Patrick holds a Ph.D., M.S. and B.S. in Computer Science from Carnegie Mellon University.
Dr. Bülent Kızıltan is a scientist and a seasoned executive who drives innovation by combining an
entrepreneurial mindset with scientific excellence. He has pushed boundaries both in academia and
industry, and has been recognized for being a catalyzer in AI innovation crossing disciplinary lines from
astrophysics to biotech.
Currently, he is leading innovation efforts focused on Causal and Predictive Analytics at Novartis. The
AI Innovation Lab is uniquely positioned at the intersection of R&D and real world impact,
collaboratively pushing AI frontiers with pioneering partners from academia and industry.
He has received numerous honors and awards for his leadership, mentorship, and teaching from
Harvard University. In 2020, he received the Presidential Grant at Novartis as a top AI Leader bringing
in global recognition and impact. In 2017, he has been recognized by the Massachusetts House of
Representatives for his outstanding contributions to the future of science. He serves as an advisory
council to Harvard Business Review and MIT Technology Review.
Dr. Kızıltan holds a PhD in astrophysics with a focus on applied mathematics, two MSc degrees in
astronomy and astrophysics with focus on statistics, and graduated summa cum laude as valedictorian
with a BSc in physics.
Dr. Charles O’Donnell brings over 15 years of data science and genomics experience to Omega, with an extensive background using data to solve scientific problems, specifically in genetic medicines. He has successfully led multi-disciplinary teams and has proven a broad range of technical and strategic expertise across data science, computational biology, machine learning, genomics and epigenomics, NGS, editing, gene therapy, proteomics, systems and network analysis, high performance computing, and data integration.
Prior to joining Omega, Charles was Head of the Computational Biology and Data Sciences function at Evelo Biosciences, and the first Head of Computational Biology and Data Sciences at Camp4 Therapeutics. Earlier in his career, he was the computational subject-matter expert in Biogen’s Epigenetics and Cell and Gene Therapy team and worked as a joint NRSA Postdoctoral Fellow in the Melton Lab of Harvard’s Stem Cell Institute and the Gifford Lab of MIT’s Computer Science and AI Laboratory.
Dr. O’Donnell received his Ph.D. in Electrical Engineering and Computer Science from MIT, working in the Berger and Devadas Labs at MIT’s Computer Science and AI Laboratory and the Lindquist Lab of the Whitehead Institute. He received his M.S. in Electrical Engineering and Computer Science from MIT and his B.S. in Computer Engineering from Columbia University.
Guillermo del Angel, PhD is Head of Bioinformatics and Data Science at Alexion, AstraZeneca Rare Disease in Boston, MA. Since joining Alexion in 2015 he has been leading research focused on applying different machine learning and data science approaches to improve rare disease target discovery, drug development and patient identification. He previously held positions at McKinsey & Co. in Mexico City and Boston, and as a computational biologist at the Broad Institute of Harvard and MIT in Cambridge, MA. A native of Mexico City, he originally trained as an electrical engineer and applied mathematician, earning a BS degree from ITESM in Mexico, a MS from Boston University and a Ph.D. from Cornell University.
Dr. Herman has more than 25 years of computational chemistry and cheminformatics experience in the pharmaceutical industry and 7 years of chemistry experience in hospitals and academic research institutions. He is currently working full-time at Sunovion Pharmaceuticals, a CNS company. From 2007 through 2016, he provided consulting services to various drug discovery companies in the Boston area. As one of the first chemists at Millennium Pharmaceuticals (now part of Takeda), he was initially responsible for all of the cheminformatics and computational chemistry support as Drug Discovery grew in the organization. Earlier, as a Harvard Fellow, he made use of his synthetic chemistry training at several major Boston area hospitals to develop novel radiopharmaceutical agents. Dr. Herman earned his PhD at Carleton University in Ottawa, Canada, where he was trained in synthetic and natural products chemistry.
Brian Tracey received the B.A. degree in physics from Kalamazoo College, Kalamazoo, MI, USA and the Ph.D. degree in oceanographic engineering (focusing on acoustics) from the Massachusetts Institute of Technology, Cambridge, MA, USA/Woods Hole Joint Program. He has worked in biomedical signal processing and imaging in the medical device industry and academia, and is currently a Director of Statistics in the Takeda Data Science Institute, Cambridge MA.
Marco Vilela holds a B.Eng in Control engineering, M.S in Computational Modeling and Doctor of Philosophy (PhD) focused in Biomathematics from Universidade Nova de Lisboa.
Marco has extensive experience in biomedical signal processing and machine learning. He most recently worked at Syllable Life Sciences, developing ML algorithms for time-series behavior phenotyping applied to 3D mice and 2D human faces videos. Prior to Syllable, he worked at on the BrainGate brain-computer interface at Brown University as well as positions UT Southwestern Meidcal School and Harvard Medical School.
One of Aria Pharmaceuticals’ first employees, Aaron C. Daugherty has helped build Aria’s drug discovery platform and leads the Discovery Science team’s efforts to discover potential treatments across a wide range of diseases. Aaron earned his PhD in Genetics from Stanford University. Prior to his time at Stanford, Aaron was a Fulbright Scholar and received his Bachelor of Science in Biology from the University of Richmond.
Christopher M. Hadad received his B.S. degree from the University of Delaware in 1987 and
then completed his Ph.D. as a Hertz Foundation fellow under the guidance of Professor
Kenneth B. Wiberg at Yale University. After a post-doctoral fellowship from the National
Science Foundation at the University of Colorado (Boulder) with Professor Charles H. DePuy,
he joined the faculty of The Ohio State University as an assistant professor of Chemistry in
1994. In 2006, he was promoted to full professor, was the vice chair for undergraduate studies
in the Department of Chemistry from 2006–2011, served the department as the interim chair in
2007, and served the College as Associate Dean (2011–2014) and then Dean (2014–2018) of
the Natural and Mathematical Sciences division of the College of Arts and Sciences.
Our research interests encompass both experimental and computational investigations in a
diverse number of areas and has resulted in about 260 peer-reviewed publications. The central
feature of the research group is the exploration of reaction mechanisms in diverse areas, from
(a) biological applications with enzymatic conversions in their active sites, (b) developing
therapeutics against exposure to organophosphorus chemical nerve agents, (c) materials
applications in dendrimers and fuel cells, (d) photochemical transformations for the generation
of novel reactive intermediates, (e) cationic and anionic processes involved in solvation
dynamics and mass spectrometry/proteomics, and (f) to the reactions of neutral reactive
intermediates involved in oxidative damage in atmospheric, combustion and biological
chemistry. The research group uses computational and experimental chemistry approaches to
understand these diverse chemical phenomena using both ab initio and density functional
theory methods, along with diverse collaborations.
Hadad teaches organic chemistry courses at both the undergraduate and graduate level. He
also teaches a computational chemistry course in order to educate students on how to use
computational chemistry to solve complex research problems. His research group currently
includes 16 graduate students and a large number of undergraduate researchers.
Sathesh Bhat, Ph.D., Executive Director in the Drug Discovery Group, joined Schrödinger in 2011. He is responsible for overseeing computational chemistry efforts on internal and partnered drug discovery programs at Schrödinger. Previously, Sathesh worked at both Merck and Eli Lilly leading computational efforts in several drug discovery programs. He obtained his Ph.D. from McGill University, which involved developing structure-based methods to predict binding free energies. Sathesh has co-authored multiple patents and publications and continues to publish on a wide variety of topics in computational chemistry.
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