Targeted drug discovery with adversarial graph autoencoders conditioned on gene expression data

Drug discovery has long been an expensive and inefficient process due to the vast chemical compound search space. This process has been iteratively sharpened and refined using computational approaches in order to narrow the scope of search. Separately, machine learning, in particular deep learning, has also made immense progress in many fields such as Computer Vision and Natural Language Processing. In particular, the new wave of generative AI models such as ChatGPT are set to revolutionize many industries in the near future. Recent advancements in deep learning have also achieved much success in many parts of the drug discovery process, from aiding in de novo drug design to modeling quantitative structure activity relationships. This project will focus on applying recent innovations in deep learning based generative AI to allow for controllable generation of potential drug molecules with desired biological properties in order to aid in the drug discovery process. Specifically, gene expression data is introduced into current state of the art de novo drug discovery models and adversarial training is applied to improve it. Additionally, as an effort to standardise the evaluation of the efficacy of future efforts in drug discovery methods catered towards conditional generation of molecules with desirable properties, a set of evaluation methods collated from existing works is proposed alongside a set of active inhibitor molecules for 9 protein targets for benchmarking.Bachelor of Engineering Science (Computer Science

Multi-center evaluation of artificial intelligent imaging and clinical models for predicting neoadjuvant chemotherapy response in breast cancer

Background: Neoadjuvant chemotherapy (NAC) plays an important role in the management of locally advanced breast cancer. It allows for downstaging of tumors, potentially allowing for breast conservation. NAC also allows for in-vivo testing of the tumors’ response to chemotherapy and provides important prognostic information. There are currently no clearly defined clinical models that incorporate imaging with clinical data to predict response to NAC. Thus, the aim of this work is to develop a predictive AI model based on routine CT imaging and clinical parameters to predict response to NAC. Methods: The CT scans of 324 patients with NAC from multiple centers in Singapore were used in this study. Four different radiomics models were built for predicting pathological complete response (pCR): first two were based on textural features extracted from peri-tumoral and tumoral regions, the third model based on novel space-resolved radiomics which extract feature maps using voxel-based radiomics and the fourth model based on deep learning (DL). Clinical parameters were included to build a final prognostic model. Results: The best performing models were based on space-resolved and DL approaches. Space-resolved radiomics improves the clinical AUCs of pCR prediction from 0.743 (0.650 to 0.831) to 0.775 (0.685 to 0.860) and our DL model improved it from 0.743 (0.650 to 0.831) to 0.772 (0.685 to 0.853). The tumoral radiomics model performs the worst with no improvement of the AUC from the clinical model. The peri-tumoral combined model gives moderate performance with an AUC of 0.765 (0.671 to 0.855). Conclusions: Radiomics features extracted from diagnostic CT augment the predictive ability of pCR when combined with clinical features. The novel space-resolved radiomics and DL radiomics approaches outperformed conventional radiomics techniques.W.L.N. is supported by the National Medical Research Council Fellowship (NMRC/MOH-000166-00)