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<span style="caret-color: rgb(33, 33, 33); color: rgb(33, 33, 33); font-family: Calibri; font-size: 14.666666984558105px;" class="">This is an announcement of </span><span class="outlook-search-highlight" style="font-family: Calibri; font-size: 14.666666984558105px; color: rgb(7, 7, 6);"><span class="outlook-search-highlight">Songhao</span></span><span style="caret-color: rgb(33, 33, 33); color: rgb(33, 33, 33); font-family: Calibri; font-size: 14.666666984558105px;" class=""> </span><span class="outlook-search-highlight" style="font-family: Calibri; font-size: 14.666666984558105px; color: rgb(7, 7, 6);"><span class="outlook-search-highlight">Jiang</span></span><span style="caret-color: rgb(33, 33, 33); color: rgb(33, 33, 33); font-family: Calibri; font-size: 14.666666984558105px;" class="">'s
Candidacy Exam.</span><br style="caret-color: rgb(33, 33, 33); color: rgb(33, 33, 33); font-family: Calibri; font-size: 14.666666984558105px;" class="">
<span style="caret-color: rgb(33, 33, 33); color: rgb(33, 33, 33); font-family: Calibri; font-size: 14.666666984558105px;" class="">===============================================</span><br style="caret-color: rgb(33, 33, 33); color: rgb(33, 33, 33); font-family: Calibri; font-size: 14.666666984558105px;" class="">
<span style="caret-color: rgb(33, 33, 33); color: rgb(33, 33, 33); font-family: Calibri; font-size: 14.666666984558105px;" class="">Candidate: </span><span class="outlook-search-highlight" style="font-family: Calibri; font-size: 14.666666984558105px; color: rgb(7, 7, 6);"><span class="outlook-search-highlight">Songhao</span></span><span style="caret-color: rgb(33, 33, 33); color: rgb(33, 33, 33); font-family: Calibri; font-size: 14.666666984558105px;" class=""> </span><span class="outlook-search-highlight" style="font-family: Calibri; font-size: 14.666666984558105px; color: rgb(7, 7, 6);"><span class="outlook-search-highlight">Jiang</span></span><br style="caret-color: rgb(33, 33, 33); color: rgb(33, 33, 33); font-family: Calibri; font-size: 14.666666984558105px;" class="">
<br style="caret-color: rgb(33, 33, 33); color: rgb(33, 33, 33); font-family: Calibri; font-size: 14.666666984558105px;" class="">
<span style="caret-color: rgb(33, 33, 33); color: rgb(33, 33, 33); font-family: Calibri; font-size: 14.666666984558105px;" class="">Date: Friday, May 17, 2024</span><br style="caret-color: rgb(33, 33, 33); color: rgb(33, 33, 33); font-family: Calibri; font-size: 14.666666984558105px;" class="">
<br style="caret-color: rgb(33, 33, 33); color: rgb(33, 33, 33); font-family: Calibri; font-size: 14.666666984558105px;" class="">
<span style="caret-color: rgb(33, 33, 33); color: rgb(33, 33, 33); font-family: Calibri; font-size: 14.666666984558105px;" class="">Time: 10 am CT</span><br style="caret-color: rgb(33, 33, 33); color: rgb(33, 33, 33); font-family: Calibri; font-size: 14.666666984558105px;" class="">
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<span style="caret-color: rgb(33, 33, 33); color: rgb(33, 33, 33); font-family: Calibri; font-size: 14.666666984558105px;" class="">Remote Location: </span><a href="https://uchicago.zoom.us/j/97665214384?pwd=UG81Z2ljUEVkMmpkVVMzSFR2QUFYdz09" title="https://uchicago.zoom.us/j/97665214384?pwd=UG81Z2ljUEVkMmpkVVMzSFR2QUFYdz09" style="color: rgb(0, 120, 215); font-family: Calibri; font-size: 14.666666984558105px;" class="">https://uchicago.zoom.us/j/97665214384?pwd=UG81Z2ljUEVkMmpkVVMzSFR2QUFYdz09</a><span style="caret-color: rgb(33, 33, 33); color: rgb(33, 33, 33); font-family: Calibri; font-size: 14.666666984558105px;" class="">
Meeting ID: 976 6521 4384 Passcode: 043310</span><br style="caret-color: rgb(33, 33, 33); color: rgb(33, 33, 33); font-family: Calibri; font-size: 14.666666984558105px;" class="">
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<span style="caret-color: rgb(33, 33, 33); color: rgb(33, 33, 33); font-family: Calibri; font-size: 14.666666984558105px;" class="">Location: JCL 298</span><br style="caret-color: rgb(33, 33, 33); color: rgb(33, 33, 33); font-family: Calibri; font-size: 14.666666984558105px;" class="">
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<span style="caret-color: rgb(33, 33, 33); color: rgb(33, 33, 33); font-family: Calibri; font-size: 14.666666984558105px;" class="">Title: Leveraging Transformer Models for Accelerated Drug Discovery</span><br style="caret-color: rgb(33, 33, 33); color: rgb(33, 33, 33); font-family: Calibri; font-size: 14.666666984558105px;" class="">
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<span style="caret-color: rgb(33, 33, 33); color: rgb(33, 33, 33); font-family: Calibri; font-size: 14.666666984558105px;" class="">Abstract: In the realm of AI-accelerated drug discovery, particularly in de novo drug design, significant challenges include
unpredictable drug responses in clinical trials, biases in predictive models, and the opaque nature of AI methodologies that complicate the understanding of a drug's mechanism of action. These issues have limited the progression of AI-discovered drugs into
clinical trials and regulatory approval. Concurrently, the development of me-too drugs, which involve modifications of existing drugs within the same therapeutic class, presents a less risky and potentially more effective avenue. However, the potential of
AI to enhance their development remains largely underexplored.</span><br style="caret-color: rgb(33, 33, 33); color: rgb(33, 33, 33); font-family: Calibri; font-size: 14.666666984558105px;" class="">
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<span style="caret-color: rgb(33, 33, 33); color: rgb(33, 33, 33); font-family: Calibri; font-size: 14.666666984558105px;" class="">This dissertation aims to transform the development of me-too drugs through the application of AI, with a focus on transformer
and large language models (LLMs). It introduces innovative frameworks that utilize the representation learning and generative capabilities of transformer models to refine and expedite the me-too drug development process. These methodologies, referred to as
"drug optimization", seek to further accelerate the production of effective me-too drugs.</span><br style="caret-color: rgb(33, 33, 33); color: rgb(33, 33, 33); font-family: Calibri; font-size: 14.666666984558105px;" class="">
<br style="caret-color: rgb(33, 33, 33); color: rgb(33, 33, 33); font-family: Calibri; font-size: 14.666666984558105px;" class="">
<span style="caret-color: rgb(33, 33, 33); color: rgb(33, 33, 33); font-family: Calibri; font-size: 14.666666984558105px;" class="">This work makes four significant contributions to the field: (1) It proposes two fusion methods that integrate transformer models
with graph neural networks, enhancing the precision of binding affinity predictions. (2) It assembles a comprehensive dataset of 10 million binding affinity values across a diverse array of proteins and drugs, providing an invaluable resource for model training
and validation. (3) It proposes two generative models for drug optimization, fine-tuned through reinforcement learning, with the goal of automating and expediting the creation of effective me-too drugs. (4) It introduces an innovative bidirectional GPT model
for molecular textual sequences (SMILES), enabling precise generative mask infilling for targeted drug optimization. And by conducting comprehensive evaluations on real world viral and cancer target proteins, we demonstrate that the proposed drug optimization
frameworks can consistently enhance existing molecules/drugs. </span><br style="caret-color: rgb(33, 33, 33); color: rgb(33, 33, 33); font-family: Calibri; font-size: 14.666666984558105px;" class="">
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<span style="caret-color: rgb(33, 33, 33); color: rgb(33, 33, 33); font-family: Calibri; font-size: 14.666666984558105px;" class="">Advisors: Rick Stevens</span><br style="caret-color: rgb(33, 33, 33); color: rgb(33, 33, 33); font-family: Calibri; font-size: 14.666666984558105px;" class="">
<br style="caret-color: rgb(33, 33, 33); color: rgb(33, 33, 33); font-family: Calibri; font-size: 14.666666984558105px;" class="">
<span style="caret-color: rgb(33, 33, 33); color: rgb(33, 33, 33); font-family: Calibri; font-size: 14.666666984558105px;" class="">Committee Members: Rick Stevens, Ian Foster, and Fangfang Xia</span><br style="caret-color: rgb(33, 33, 33); color: rgb(33, 33, 33); font-family: Calibri; font-size: 14.666666984558105px;" class="">
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