There exists a lot of literature on distinguishing driver mutations from passengers. I am trying to build my own deep learning model to do the same. I am facing some potential issues. First, I have downloaded COSMIC mutation data and used the FATHMM labels to designate drivers (positive examples) in my dataset. I am sceptical to use passengers from COSMIC as they may be false negatives. So I turned to the 1000 genome project to download SNVs (to construct my negative examples). I am unsure if this is correct, however, I have seen some papers do the same. Do I need to apply any filters on the 1000 genome SNV data to construct the final dataset? One such paper talks of using SNVs with a global minor allele frequency≤1%.
You'll fist need to clarify what exactly you mean by driver mutation vs passenger mutation. 1) Are you concerned with somatic mutations or germline mutations (most often it is somatic, if you say "driver mutation")? 2) Are you concerned mostly about protein-coding mutations or non-coding mutations? 

1) If you are interested in somatic driver mutations, I highly recommend that you do NOT use germline variants from sources such as the 1000 genome project as "passengers". Germline variants are systematically different than somatic mutations because they undergo substantially more negative selection than somatic mutations do (PMID: 19654296). If you are interested in "pathogenic" germline variants that relate to cancer, then using high allele frequency germline variants as "passengers" is an ok but not perfect solution (see Kevin's comments).

2) If you are interested in protein-coding driver mutations, I highly recommend that you do not develop a method that also tries to predict non-coding driver mutations. The recent PCAWG papers have highlighted that indeed most of the somatic driver mutations in cancer happen in protein-coding regions. Moreover, non-coding prediction methods applied to protein coding regions generally don't fare well in benchmarks (see PMID: 32079540). The top methods in that recent benchmark at predicting driver mutations were CHASM, CTAT-cancer (see my paper, PMID: 29625053), DEOGEN2 and PrimateAI. I recently released CHASMplus (pmid: 31202631) which is a substantial improvement over top performing CHASM at predicting driver mutations, and my training labels are available (https://chasmplus.readthedocs.io/en/latest/faq.html , see "Where can I obtain the training data for CHASMplus?").
